Method of identifying and treating a person having a predisposition to or afflicted with a cardiometabolic disease

ABSTRACT

The invention relates to method for identifying and selecting a subject with increased risk of developing a cardiometabolic disease and optionally, providing a personalized medicine method, which may involve sequencing at least part of a genome of one or more cells in a blood sample of the subject and identifying from said sequencing one or more mutations in one or more somatic mutations.

RELATED APPLICATIONS AND INCORPORATION BY REFERENCE

This application claims benefit of and priority to U.S. provisional patent application Ser. No. 62/084,127 filed Nov. 25, 2014.

SEQUENCE LISTING

The instant application contains a Sequence Listing which has been submitted electronically in ASCII format and is hereby incorporated by reference in its entirety. Said ASCII copy, created on Jan. 14, 2021, is named BRB-00801_SL.txt and is 13,143 bytes in size.

The foregoing applications, and all documents cited therein or during their prosecution (“appln cited documents”) and all documents cited or referenced in the appln cited documents, and all documents cited or referenced herein (“herein cited documents”), and all documents cited or referenced in herein cited documents, together with any manufacturer's instructions, descriptions, product specifications, and product sheets for any products mentioned herein or in any document incorporated by reference herein, are hereby incorporated herein by reference, and may be employed in the practice of the invention. More specifically, all referenced documents are incorporated by reference to the same extent as if each individual document was specifically and individually indicated to be incorporated by reference.

FIELD OF THE INVENTION

The present invention relates to identifying individuals with a predisposition to cardiovascular disease. In particular, the invention relates to method for identifying and selecting a subject with increased risk of developing a cardiometabolic disease and optionally a hematological cancer, and in some instances, providing a personalized medicine method.

BACKGROUND OF THE INVENTION

Cancer is thought to arise via stepwise acquisition of genetic or epigenetic changes that transform a normal cell (Nowell P C. Science 1976; 194:23-8). Hence, the existence of a pre-malignant state bearing only the initiating lesions may be detectable in some individuals with no other signs of disease. For example, multiple myeloma (MM) is frequently preceded by monoclonal gammopathy of unknown significance (MGUS) (Kyle R A et al. The New England journal of medicine 2002; 346:564-9), and chronic lymphocytic leukemia (CLL) is commonly preceded by monoclonal B-lymphocytosis (MBL) (Rawstron A C et al. The New England journal of medicine 2008; 359:575-83).

Several lines of evidence have suggested that clonal hematopoiesis due to an expansion of cells harboring an initiating driver mutation might be an aspect of the aging hematopoietic system. Clonal hematopoiesis in the elderly was first demonstrated in studies that found that approximately 25% of healthy women over the age of 65 have a skewed pattern of X-chromosome inactivation in peripheral blood cells (Busque L et al. Blood 1996; 88:59 65, Champion K M et al. British journal of haematology 1997; 97:920 6) which in some cases is associated with mutations in TET2 (Busque L et al. Nature genetics 2012; 44: 1179-81). Large-scale somatic events such as chromosomal insertions and deletions and loss of heterozygosity (LOH) also occur in the blood of −2% of individuals over the age of 75 (Jacobs K B et al. Nature genetics 2012; 44:651-8, Laurie C C et al. Nature genetics 2012; 44:642-50). Pre-leukemic hematopoietic stem cells (HSCs) harboring only the initiating driver mutation have been found in the bone marrow of patients with AML in remission (Jan M et al. Science translational medicine 2012; 4: 149ral8, Shlush L I et al. Nature 2014; 506:328-33). Furthermore, a substantial proportion of the population carries cells with t(14;18) translocations, although the lesion is generally present in fewer than 1 in 1000 cells (Roulland S et al. t(14;18) Journal of clinical oncology: official journal of the American Society of Clinical Oncology 2014; 32: 1347-55).

Recent sequencing studies have identified a set of recurrent mutations in several types of hematological malignancies (Mardis E R et al. The New England journal of medicine 2009; 361: 1058-66, Bejar R et al. The New England journal of medicine 2011; 364:2496-506, Papaemmanuil E et al. The New England journal of medicine 2011; 365: 1384-95, Walter et al. Leukemia 2011; 25: 1153-8, Welch J S et al. Cell 2012; 150:264-78, Cancer Genome Atlas Research N. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. The New England journal of medicine 2013; 368:2059-74, Papaemmanuil E et al. Blood 2013; 122:3616-27; quiz 99, Walter M J et al. Leukemia 2013; 27: 1275-82, Zhang J et al. Proceedings of the National Academy of Sciences of the United States of America 2013; 110: 1398-403, Morin R D et al. Nature 2011; 476:298-303, Lenz G et al. Science 2008; 319: 1676-9, Lohr J G et al. Proceedings of the National Academy of Sciences of the United States of America 2012; 109:3879-84, Neumann M et al. Blood 2013; 121:4749-52). However, the frequency of these somatic mutations in the general population is unknown. Citation or identification of any document in this application is not an admission that such document is available as prior art to the present invention.

SUMMARY OF THE INVENTION

The present invention relates to Applicants' hypothesis that somatically acquired single nucleotide variants (SNVs) and small insertions/deletions (indels) might be increasingly detectable in the blood of otherwise healthy individuals as a function of age.

The invention relates to method for identifying and selecting a subject with increased risk of developing a cardiometabolic disease and optionally a hematological cancer, which may comprise the steps of: (a) sequencing at least part of a genome which may comprise one or more genes selected from the group consisting of DNMT3A, TET2, ASXLI, TP53, JAK2 and SF3B1 of one or more cells in a blood sample of the subject, (b) identifying from said sequencing one or more mutations in one or more genes selected from the group consisting of DNMT3A, TET2, ASXLI, TP 53, JAK2 and SF3B1, wherein presence of said mutation(s) indicates an increased risk of developing a cardiometabolic disease and optionally a hematological cancer.

The invention also relates to a method for identifying and selecting a subject with an increased risk of developing a cardiometabolic disease and optionally a hematological cancer and providing a personalized medicine method, said method which may comprise the steps of (a) sequencing at least part of a genome which may comprise one or more genes selected from the group consisting of DNMT3A, TET2, ASXLI, TP53, JAK2 and SF3B1 of one or more cells in a blood sample of the subject, (b) identifying from said sequencing one or more mutations in one or more genes selected from the group consisting of DNMT3A, TET2, ASXLI, TP 53, JAK2 and SF3B1, wherein presence of said mutation(s) indicates an increased risk of developing a cardiometabolic disease and optionally a hematological cancer, and (c) initiating a treatment or monitoring regimen to suppress said mutation(s) in the subject, thereby decreasing risk of developing a cardiometabolic disease and optionally a hematological cancer.

The presence of said mutation(s) in the above embodiments may indicate an increase in red blood cell distribution width (RDW).

The cardiometabolic disease may be atherosclerosis, coronary heart disease (CHD) or ischemic stroke (IS) or type 2 diabetes (T2D).

In embodiments wherein an increased risk for hematological cancer is also screened in addition to a cardiometabolic disease, the hematological cancer may be a leukemia, a lymphoma, a myeloma or a blood syndrome. The leukemia may be an acute myeloid leukemia (AML), chronic myelogenous leukemia (CML) or chronic lymphocytic leukemia (CLL). The blood syndrome may be myelodysplastic syndrome (MDS) or myeloproliferative neoplasm (MPN).

The one more cells in the blood sample may be derived from hematopoietic stem cells (HSCs), committed myeloid progenitor cells having long term self-renewal capacity or mature lymphoid cells having long term self-renewal capacity.

In some embodiments the part of the genome that is sequenced may be an exome. In other embodiments, the sequencing may be whole exome sequencing (WES) or targeted gene sequencing.

In an advantageous embodiment, the subject is a human. In other embodiments, the human may exhibit one or more risk factors of being a smoker, having a high level of total cholesterol or having high level of high-density lipoprotein (HDL).

The mutations of at least DNMT3A, TET2, ASXL1, TP53, JAK2 and SF3B1 may be frameshift mutations, nonsense mutations, missense mutations or splice-site variant mutations.

If the mutation is in DNMT3A, the mutation may advantageously be a mutation in exons 7 to 23. In a particularly advantageous embodiment, the mutation in DNMT3A is a mutation selected from the group consisting of P307S, P307R, R326H, R326L, R326C, R326S, R366P, R366H, R366G, A368T, F414L, F414S, F414C, C497Y, Q527H, Q527P, Y533C, G543A, G543S, G543C, L547H, L547P, L547F, M548I, M548K, G550R, W581R, W581G, W581C, G646V, G646E, L653W, L653F, V657A, V657M, R659H, Y660C, R676W, R676Q, G685R, G685E, G685A, D686Y, D686G, G699R, G699S, G699D, P700S, P700R, P700Q, D702N, D702Y, V704M, V704G, I705F, I705T, I705S, C710S, S714C, N717S, N717I, P718L, R720H, R720G, Y724C, R729Q, R729W, R729G, F731L, F732del, F732S, F732L, F734L, F734C, Y735C, Y735N, Y735S, R736H, R736C, R736P, L737H, L737V, L737F, L737R, A741V, R749C, R749L, F751L, F752del, F752C, F752L, F752I, F752V, L754R, L754H, F755S, F755I, F755L, M761I, M761V, G762C, S770W, S770P, R771Q, F772I, F772V, L773R, E774K, E774D, D781G, R792H, G796D, G796V, N797Y, N797H, P799R, P799H, R803S, P804S, P804L, S828N, K829R, Q842E, P849L, D857N, W860R, F868S, G869S, G869V, M880V, S881R, S881I, R882H, R882P, R882C, R882G, Q886R, G890D, L901R, L901H, P904L, F909C and A910P.

If the mutation is in TET2, the mutation is advantageously selected from the group consisting of S282F, N312S, L346P, S460F, D666G, P941S, and C1135Y.

If this mutation is in ASXL1, the mutation is advantageously a mutation in exon 11-12.

If the mutation is in TP53, the mutation is advantageously a mutation selected from the group consisting of S46F, G105C, G105R, G105D, G108S, G108C, R110L, R110C, T118A, T118R, T118I, L130V, L130F, K132Q, K132E, K132W, K132R, K132M, K132N, C135W, C135S, C135F, C135G, Q136K, Q136E, Q136P, Q136R, Q136L, Q136H, A138P, A138V, A138A, A138T, T140I, C141R, C141G, C141A, C141Y, C141S, C141F, C141W, V143M, V143A, V143E, L145Q, L145R, P151T, P151A, P151S, P151H, P152S, P152R, P152L, T155P, R158H, R158L, A159V, A159P, A159S, A159D, A161T, A161D, Y163N, Y163H, Y163D, Y163S, Y163C, K164E, K164M, K164N, K164P, H168Y, H168P, H168R, H168L, H168Q, M169I, M169T, M169V, T170M, E171K, E171Q, E171G, E171A, E171V and E171D, V172D, V173M, V173L, V173G, R174W, R175G, R175C, R175H, C176R, C176G, C176Y, C176F, C176S, P177R, P177R, P177L, H178D, H178P, H178Q, H179Y, H179R, H179Q, R181C, R181Y, D186G, G187S, P190L, P190T, H193N, H193P, H193L, H193R, L194F, L194R, I195F, I195N, I195T, V197L, G199V, Y205N, Y205C, Y205H, D208V, R213Q, R213P, R213L, R213Q, H214D, H214R, S215G, S215I, S215R, V216M, V217G, Y220N, Y220H, Y220S, Y220C, E224D, I232F, I232N, I232T, I232S, Y234N, Y234H, Y234S, Y234C, Y236N, Y236H, Y236C, M237V, M237K, M237I, C238R, C238G, C238Y, C238W, N239T, N239S, S241Y, S241C, S241F, C242G, C242Y, C242S, C242F, G244S, G244C, G244D, G245S, G245R, G245C, G245D, G245A, G245V, G245S, M246V, M246K, M246R, M246I, N247I, R248W, R248G, R248Q, R249G, R249W, R249T, R249M, P250L, I251N, L252P, 1254S, I255F, I255N, I255S, L257Q, L257P, E258K, E258Q, D259Y, S261T, G262D, G262V, L265P, G266R, G266E, G266V, R267W, R267Q, R267P, E271K, V272M, V272L, R273S, R273G, R273C, R273H, R273P, R273L, V274F, V274D, V274A, V274G, V274L, C275Y, C275S, C275F, A276P, C277F, P278T, P278A, P278S, P278H, P278R, P278L, G279E, R280G, R280K, R280T, R280I, R280S, D281N, D281H, D281Y, D281G, D281E, R282G, R282W, R282Q, R282P, E285K, E285V, E286G, E286V, E286K, K320N, L330R, G334V, R337QR337L, A347T, L348F, T377P.

If the mutation is in JAK2, the mutation is advantageously selected from the group consisting of N533D, N533Y, N533S, H538R, K539E, K539L, I540T, I540V, V617F, R683S, R683G, del/ins537-539L, del/ins538-539L, del/ins540-543MK, del/ins540-544MK, del/ins541-543K, del542-543, del543-544 and ins 11546-547.

If the mutation is in SF3B1, the mutation is advantageously selected from the group consisting of G347V, R387W, R387Q, E592K, E622D, Y623C, R625L, R625C, H662Q, H662D, K666N, K666T, K666E, K666R, K700E, V701F, A708T, G740R, G740E, A744P, D781G and E783K.

Accordingly, it is an object of the invention to not encompass within the invention any previously known product, process of making the product, or method of using the product such that Applicants reserve the right and hereby disclose a disclaimer of any previously known product, process, or method. It is further noted that the invention does not intend to encompass within the scope of the invention any product, process, or making of the product or method of using the product, which does not meet the written description and enablement requirements of the USPTO (35 U.S.C. § 112, first paragraph) or the EPO (Article 83 of the EPC), such that Applicants reserve the right and hereby disclose a disclaimer of any previously described product, process of making the product, or method of using the product.

It is noted that in this disclosure and particularly in the claims and/or paragraphs, terms such as “comprises”, “comprised”, “comprising” and the like can have the meaning attributed to it in U.S. Patent law; e.g., they can mean “includes”, “included”, “including”, and the like; and that terms such as “consisting essentially of and “consists essentially of have the meaning ascribed to them in U.S. Patent law, e.g., they allow for elements not explicitly recited, but exclude elements that are found in the prior art or that affect a basic or novel characteristic of the invention.

These and other embodiments are disclosed or are obvious from and encompassed by, the following Detailed Description.

BRIEF DESCRIPTION OF THE DRAWINGS

The following detailed description, given by way of example, but not intended to limit the invention solely to the specific embodiments described, may best be understood in conjunction with the accompanying drawings.

FIG. 1. Prevalence of somatic mutation by age. Colored bands represent 50th, 75th, and 95th percentiles.

FIG. 2. Characteristics of called somatic variants. A) Ten most frequently mutated genes. B) Number of subjects with 1, 2, 3, or 4 called variants. C) Percentages of type of single nucleotide base pair changes seen in the called variants. D) Allele fractions of called somatic variants. Allele fraction defined as variant reads divided by variant plus reference reads. For variants on the X chromosome in men this number was divided by 2.

FIG. 3. Development of hematologic malignancies. A) Forest plot for risk of developing hematologic malignancy in those with somatic mutations overall and with VAF>0.10, relative to those without mutations. Diamond represents the results of fixed-effects meta-analysis for the 2 cohorts, and horizontal lines are 95 percent confidence intervals. Hazard ratios were estimated by competing risks regression with death as the competing risk. The analysis includes adjudicated cancer information from MEC and unadjudicated information by annual subject interview in JHS. For interview data, leukemia, lymphoma, multiple myeloma, blood cancer, and spleen cancer were considered hematologic malignancy. All models included age groups (less than 50, 50-59, 60-69, greater than 70), diabetes status, and sex as covariates. B) Cumulative incidence plot for developing hematologic malignancy. Curves were generated from competing risks data with death as the competing risk. C) VAF in subjects that did or did not develop hematologic malignancy, p-value from Wilcoxon test.

FIG. 4. Effect of somatic mutations on all-cause mortality. A) Forest plot for all-cause mortality risk associated with having a somatic clone. Diamond represents the results of fixed-effects meta-analysis of all cohorts, and horizontal lines are 95 percent confidence intervals. All models included age groups (less than 60, 60-69, 70-79, 80-89, and 90 or greater), diabetes status, and sex as covariates in a Cox proportional hazards model. Botnia includes Helsinki-sib and Diabetes-reg. B) Kaplan-Meier survival curves from the same cohorts as above, with p-values from log rank test. Left panel is those younger than 70 at time of DNA ascertainment, and right panel is those 70 or older. C) Cox proportional hazards model for all-cause mortality for those with or without mutations stratified by normal or high RDW. Diamond represents the results of fixed-effects meta-analysis of all cohorts, and horizontal lines are 95 percent confidence intervals. All models included age groups (less than 60, 60-69, 70-79, 80-89, and 90 or greater), diabetes status, and sex as covariates.

FIG. 5. Association of somatic mutations with incident cardiovascular disease. A-B) Cumulative incidence plots for incident coronary heart disease (CHD) (A) and ischemic stroke (B). Curves were generated from competing risks data with death as the competing risk. Those with prevalent events were excluded from the analyses. C-D) Forest plots for risk of developing incident CHD (C) and ischemic stroke (D) in those with somatic mutations. Diamond represents the results of fixed-effects meta-analysis using beta-coefficients from competing risks regressions for both cohorts, and horizontal lines are 95 percent confidence intervals. Age groups (less than 50, 50-59, 60-69, and 70 or greater), T2D status, sex, systolic blood pressure groups (less than 140 mm Hg, 140-160 mm Hg, and greater than 160 mm Hg), and body mass index groups (less than 25, 25-35, and greater than 35) were included as categorical covariates in the competing risks regression models, with death as the competing risk. Those with prevalent events were excluded from the analyses.

FIG. 6. Characteristics of DNMT3A variants. A) Frequency of DNMT3A nonsense, frameshift, and splice-site variants called as somatic by age group. B) Frequency of R882 and non-R882 missense variants called as somatic by age group. C) Allele fraction of called DNMT3A variants by mutation type.

FIG. 7. Co-mutations. A) Co-mutation plot, individuals are represented by columns. Black rectangles represent mutated genes, red rectangles represent 2 separate mutations in the same gene. B) Correlation plot for variant allele fraction (VAF) from the 49 subjects with 2 mutations.

FIG. 8. Factors associated with clonality A) Frequency of mutation for males and females by age group. For those 60 or older, being male is associated with having a detectable clone (OR 1.3, 95% CI 1.1-1.5, p=0.005 by multivariable logistic regression using age, sex, T2D and BMI as covariates). B) Frequency of mutation for those with and without type 2 diabetes by age group. C) Frequency of mutation for non-Hispanics, Hispanics, and South Asians by age group.

FIG. 9. Mutations by ethnic backgroundNumber of mutations for each gene stratified by ethnic background.

FIG. 10. Blood counts for individuals with and without detectable mutations. Dots represent individuals. Box represents 25th and 75th percentiles, line in box represents median. Whiskers represent 5th and 95th percentiles. For listed genes, individuals only had mutations in that gene, and not other genes. Dashed red lines represent 11.5% and 14.5%, the normal ranges for RDW. Abbreviations: WBC—white blood cell count, PLT—platelet count, RDW—red cell distribution width, MCV—mean corpuscular volume. Individuals were from Jackson Heart Study, Longevity Genes Project, Botnia, Helsinki-sib, or Malmo-sib.

FIG. 11. Kaplan-Meier Curves for overall survival by cohorts.

FIG. 12. Kaplan-Meier curves for individuals with or without clones, stratified by high (>14.5%) or normal (<14.5%) red cell distribution width. Hash marks represent censored individuals. Individuals were from Jackson Heart Study or Longevity Genes Project.

FIG. 13. Risk model for coronary heart disease and ischemic stroke. Regression parameters are same as shown for FIGS. 5C and 5D. Hazard ratios were estimated using competing risks regression with death as the competing risk. P-values are derived from the Fine-Gray test. Individuals with prior coronary heart disease (CHD) were excluded for CHD analysis, and individuals with prior ischemic stroke were excluded for stroke analysis. A) Coronary heart disease, B) ischemic stroke.

FIG. 14. Mutation validation and serial samples. A) Eighteen variants were validated using targeted, amplicon based re-sequencing (“Rapid Heme Panel”, see Methods). Comparison of VAF between the two methods is shown for the 18 variants. WES, whole exome sequencing. RHP, Rapid Heme Panel. B) Peripheral blood DNA from 4 to 8 years after the initial DNA collection was available for 13 subjects in JHS who had detectable mutations on exome sequencing. As described in the Supplementary Methods, amplicon-based targeted re-sequencing was performed for a panel of 95 genes. Graphs represent individual subjects, with mutation variant allele frequency (VAF) from the initial and later time points shown. All of the initial mutations detected on exome sequencing were still present in the later sample, and 2 subjects had acquired new mutations.

FIG. 15. Risk of cancer, cardiovascular, or other death associated with clonality. Hazard ratios obtained by competing risks regression, with death by other causes as the competing risk. Results shown are risk associated with clonality. For cancer deaths, only non-hematologic cancers were included. Cardiovascular deaths included strokes (hemorrhagic or ischemic) and fatal myocardial infarction. All regressions included age groups (less than 50, 50-59, 60-69, 70 or older), diabetes status, and gender as covariates. Individuals were from Botnia, FUSION, MEC, and Jackson Heart Study. For Jackson Heart Study, only cardiovascular outcomes were adjudicated (all other deaths were considered unadjudicated).

FIG. 16. (A) Forest plot for odds ratio (OR) of having a somatic mutation in those presenting with myocardial infarction (MI) compared to those without MI (referent). Results are shown for 2 cohorts (ATVB and PROMIS) and stratified by age groups. (B) Counts for number of mutations seen in cases versus controls for the most frequently mutated genes.

FIG. 17. (A) Low power sections of the aortic root from recipients of Tet2−/−marrow (top row) or control marrow (bottom row) are shown at three time-points after initiation of diet. The left and center columns are oil red O stained, and the right column is Masson's trichrome. (B) Spleen (top row, H&E) and digit (bottom row, oil red O) histology are shown in mice after 14 weeks on diet. Note the large fat deposits (white arrowheads) and sheets of lipid-laden macrophages in the tissues (black arrowheads) of Tet2−/− recipients.

FIG. 18. Risk of cardiovascular events from carrying a somatic clonal mutation. Risk if cardiovascular events (myocardial infarction, coronary revascularization, or stroke) in each cohort and fixed effects meta-analysis are displayed. Effect estimates are derived from a Cox proportional hazards model after accounting for age, sex, ethnicity, diabetes mellitus, smoking, hypertension, LDL cholesterol. There was no evidence of heterogeneity between the effect estimates in the two cohorts (P for heterogeneity=0.79).

FIG. 19. Coronary arterial calcification quantity by somatic clonal mutation carrier status. Among participants in the BioImage cohort, coronary arterial calcification (CAC) quantity was obtained as the total Agatston score. The relative difference in CAC in somatic clonal mutation carriers compared to non-carriers is estimated from linear regression with log-transformation of CAC quantity.

DETAILED DESCRIPTION OF THE INVENTION

The incidence of hematological malignancies increases with age and is associated with recurrent somatic mutations in specific genes. Applicants hypothesized that such mutations would be detectable in the blood of some individuals not known to have hematological disorders.

Applicants analyzed whole exome sequencing data from peripheral blood cell DNA of 17, 182 individuals who were unselected for hematologic phenotypes. Applicants looked for somatic mutations by identifying previously characterized single nucleotide variants (SNVs) and small insertions/deletions (indels) in 160 genes recurrently mutated in hematological malignancies. The presence of mutations was analyzed for association to hematological phenotypes, survival, and cardiovascular events.

Detectable somatic mutations were rare in individuals younger than 40, but rose appreciably with age. At ages 70-79, 80-89, and 90-108 these clonal mutations were observed in 9.6% (220 out of 2299), 11.7% (37 out of 317), and 18.4% (19 out of 103) of individuals, respectively. The majority of the variants occurred in 3 genes: DNMT3A, TET2, and ASXL1. The presence of a somatic mutation was associated with increased risk of developing hematologic malignancy (HR 11, 95% confidence interval [95%>CI] 3.9-33), increased all-cause mortality (HR 1.4, 95% CI 1.1-1.8), and increased risk of incident coronary heart disease (HR 2.0, 95% CI 1.2-3.4) and ischemic stroke (HR 2.6, 95% CI 1.4-4.8).

Clonal hematopoiesis of indeterminate potential (CHIP) is a common pre-malignant condition in the elderly, and is associated with increased risk of transformation to hematologic malignancy and increased all-cause mortality, possibly due to increased cardiometabolic disease.

Cardiovascular disease is the leading cause of death worldwide. Given the association of somatic mutations with all-cause mortality beyond that explicable by hematologic malignancy and T2D, Applicants performed association analyses from two cohorts comprising 3,353 subjects with available data on coronary heart disease (CHD) and ischemic stroke (IS). After excluding those with prevalent events, Applicants found that those carrying a mutation had increased cumulative incidence of both CHD and IS (FIGS. 5A and 5B). In multivariable analyses that included age, sex, T2D, systolic blood pressure, and body mass index as covariates, the hazard ratio of incident CHD and IS was 2.0 (95% CI 1.2-3.5, P=0.015) and 2.6 (95% CI 1.3-4.8, P=0.003) in the individuals carrying a somatic mutation as compared to those without (FIGS. 5C and 5D, FIG. 8).

For a subset of individuals, the traditional risk factors of smoking, total cholesterol, and high-density lipoprotein were also available; the presence of a somatic mutation remained significantly associated with incident CHD and IS even in the presence of these risk factors, and the risk was even greater in those with VAF>0.10 (Supplementary Table S12). Elevated RDW and high-sensitivity C-reactive protein (hsCRP) have also been associated with adverse cardiac outcomes (ToneUi M et al. Circulation 2008; 117: 163-8, Ridker P M et al. The New England journal of medicine 2002; 347: 1557-65), possibly reflecting an underlying inflammatory cause. In a multivariable analysis of 1,795 subjects from JHS, those with a mutation and RDW>14.5% had a markedly increased risk of incident CHD, and this effect was independent of hsCRP (Supplementary Table SI 3).

Applicants find that somatic mutations leading to clonal outgrowth of hematopoietic cells are frequent in the general population. This entity, which Applicants term clonal hematopoiesis with indeterminate potential (CHIP), is present in over 10% of individuals over 70, making it one of the most common known pre-malignant lesions. The exact prevalence of CHIP is dependent on how cancer-causing mutations are defined and on the sensitivity of the technique used to detect mutations, and thus may substantially exceed this estimate. Unlike other pre-malignant lesions, CHIP appears to involve a substantial proportion of the affected tissue in most individuals; based on the proportion of alleles with the somatic mutation, Applicants find that a median of 18% of peripheral blood leukocytes are part of the abnormal clone. CHIP also persists over time; in all tested cases, the mutations were still present after 4 to 8 years.

The genes most commonly mutated in CHIP are DNMT3A, TET2, and ASXL1. This is consistent with previous studies that have found DNMT3A and TET2 mutations to be frequent and early events in AML and MDS (Jan M et al. Science translational medicine 2012; 4: 149ral8, Shlush L I et al. Nature 2014; 506:328-33, Papaemmanuil E et al. The New England journal of medicine 2011; 365: 1384-95, Welch J S et al. Cell 2012; 150:264-78). Murine models of DNMT3A or TET2 loss-of-function demonstrate that mutant HSCs have altered methylation patterns at pluripotency genes and a competitive advantage compared to wild-type HSCs, but mice rarely develop frank malignancy, and then only after long latency (Jeong M et al. Nature genetics 2014; 46:17-23, Koh K P et al. Cell stem cell 2011; 8:200-13, Challen G A et al. Nature genetics 2012; 44:23-31, Moran-Crusio K et al. Cancer cell 2011; 20: 11-24). Similarly, Applicants' data show that humans with CHIP can live for many years without developing hematological malignancies, though they do have increased risk relative to those without mutations.

TET2 and DNMT3A are frequently mutated in some lymphoid malignancies, and the initiating event for such tumors may occur in a HSC (Neumann M et al. Blood 2013; 121:4749-52, Quivoron C et al. Cancer cell 2011; 20:25-38, Odejide O et al. Blood 2014; 123: 1293-6, Asmar F et al. Haematologica 2013; 98: 1912-20, Couronne L et al. The New England journal of medicine 2012; 366:95-6). While it is most likely that these mutations occur in a HSC, it also possible that they occur in committed myeloid progenitors or mature lymphoid cells that have acquired long-term self-renewal capacity.

The use of somatic mutations to aid in the diagnosis of patients with clinical MDS is becoming widespread. Applicants' data demonstrate that the majority of individuals with clonal mutations in peripheral blood do not have MDS or another hematological malignancy, nor do the majority develop a clinically diagnosed malignancy in the near term. At this time, it would be premature to genetically screen healthy individuals for the presence of a somatic clone, as the positive predictive value for current or future malignancy is low.

The invention relates to method for identifying and selecting a subject with increased risk of developing a cardiometabolic disease and optionally a hematological cancer, which may comprise the steps of: (a) sequencing at least part of a genome which may comprise one or more genes selected from the group consisting of DNMT3A, TET2, ASXLI, TP53, JAK2 and SF3B1 of one or more cells in a blood sample of the subject, (b) identifying from said sequencing one or more mutations in one or more genes selected from the group consisting of DNMT3A, TET2, ASXLI, TP 53, JAK2 and SF3B1, wherein presence of said mutation(s) indicates an increased risk of developing a cardiometabolic disease and optionally a hematological cancer.

The invention also relates to a method for identifying and selecting a subject with an increased risk of developing a cardiometabolic disease and optionally a hematological cancer and providing a personalized medicine method, said method which may comprise the steps of (a) sequencing at least part of a genome which may comprise one or more genes selected from the group consisting of DNMT3A, TET2, ASXLI, TP53, JAK2 and SF3B1 of one or more cells in a blood sample of the subject, (b) identifying from said sequencing one or more mutations in one or more genes selected from the group consisting of DNMT3A, TET2, ASXLI, TP 53, JAK2 and SF3B1, wherein presence of said mutation(s) indicates an increased risk of developing a cardiometabolic disease and optionally a hematological cancer, and (c) initiating a treatment or monitoring regimen to suppress said mutation(s) in the subject, thereby decreasing risk of developing a cardiometabolic disease and optionally a hematological cancer.

The presence of said mutation(s) in the above embodiments may indicate an increase in red blood cell distribution width (RDW).

The cardiometabolic disease may be atherosclerosis, coronary heart disease (CHD) or ischemic stroke (IS).

In embodiments wherein an increased risk for hematological cancer is also screened in addition to a cardiometabolic disease, the hematological cancer may be a leukemia, a lymphoma, a myeloma or a blood syndrome. The leukemia may be an acute myeloid leukemia (AML) or chronic myelogenous leukemia (CML). The blood syndrome may be myelodysplasia syndrome (MDS).

The one more cells in the blood sample may be hematopoietic stem cells (HSCs), committed myeloid progenitor cells having long term self-renewal capacity or mature lymphoid cells having long term self-renewal capacity.

In some embodiments the part of the genome that is sequenced may be an exome. In other embodiments, the sequencing may be whole exome sequencing (WES).

In an advantageous embodiment, the subject is a human. In other embodiments, the human may exhibit one or more risk factors of being a smoker, having a high level of total cholesterol or having high level of high-density lipoprotein (HDL).

The mutations of at least DNMT3A, TET2, ASXL1, TP53, JAK2 and SF3B1 may be frameshift mutations, nonsense mutations, missense mutations or splice-site variant mutations.

If the mutation is in DNMT3A, the mutation may advantageously be a mutation in exons 7 to 23. In a particularly advantageous embodiment, the mutation in DNMT3A is a mutation selected from the group consisting of P307S, P307R, R326H, R326L, R326C, R326S, R366P, R366H, R366G, A368T, F414L, F414S, F414C, C497Y, Q527H, Q527P, Y533C, G543A, G543S, G543C, L547H, L547P, L547F, M548I, M548K, G550R, W581R, W581G, W581C, G646V, G646E, L653W, L653F, V657A, V657M, R659H, Y660C, R676W, R676Q, G685R, G685E, G685A, D686Y, D686G, G699R, G699S, G699D, P700S, P700R, P700Q, D702N, D702Y, V704M, V704G, I705F, I705T, I705S, C710S, S714C, N717S, N717I, P718L, R720H, R720G, Y724C, R729Q, R729W, R729G, F731L, F732del, F732S, F732L, F734L, F734C, Y735C, Y735N, Y735S, R736H, R736C, R736P, L737H, L737V, L737F, L737R, A741V, R749C, R749L, F751L, F752del, F752C, F752L, F752I, F752V, L754R, L754H, F755S, F755I, F755L, M761I, M761V, G762C, S770W, S770P, R771Q, F772I, F772V, L773R, E774K, E774D, D781G, R792H, G796D, G796V, N797Y, N797H, P799R, P799H, R803S, P804S, P804L, S828N, K829R, Q842E, P849L, D857N, W860R, F868S, G869S, G869V, M880V, S881R, S881I, R882H, R882P, R882C, R882G, Q886R, G890D, L901R, L901H, P904L, F909C and A910P.

If the mutation is in TET2, the mutation is advantageously selected from the group consisting of S282F, N312S, L346P, S460F, D666G, P941S, and C1135Y.

If this mutation is in ASXL1, the mutation is advantageously a mutation in exon 11-12.

If the mutation is in TP53, the mutation is advantageously a mutation selected from the group consisting of S46F, G105C, G105R, G105D, G108S, G108C, R110L, R110C, T118A, T118R, T118I, L130V, L130F, K132Q, K132E, K132W, K132R, K132M, K132N, C135W, C135S, C135F, C135G, Q136K, Q136E, Q136P, Q136R, Q136L, Q136H, A138P, A138V, A138A, A138T, T140I, C141R, C141G, C141A, C141Y, C141S, C141F, C141W, V143M, V143A, V143E, L145Q, L145R, P151T, P151A, P151S, P151H, P152S, P152R, P152L, T155P, R158H, R158L, A159V, A159P, A159S, A159D, A161T, A161D, Y163N, Y163H, Y163D, Y163S, Y163C, K164E, K164M, K164N, K164P, H168Y, H168P, H168R, H168L, H168Q, M169I, M169T, M169V, T170M, E171K, E171Q, E171G, E171A, E171V and E171D, V172D, V173M, V173L, V173G, R174W, R175G, R175C, R175H, C176R, C176G, C176Y, C176F, C176S, P177R, P177R, P177L, H178D, H178P, H178Q, H179Y, H179R, H179Q, R181C, R181Y, D186G, G187S, P190L, P190T, H193N, H193P, H193L, H193R, L194F, L194R, I195F, I195N, I195T, V197L, G199V, Y205N, Y205C, Y205H, D208V, R213Q, R213P, R213L, R213Q, H214D, H214R, S215G, S215I, S215R, V216M, V217G, Y220N, Y220H, Y220S, Y220C, E224D, I232F, I232N, I232T, I232S, Y234N, Y234H, Y234S, Y234C, Y236N, Y236H, Y236C, M237V, M237K, M237I, C238R, C238G, C238Y, C238W, N239T, N239S, S241Y, S241C, S241F, C242G, C242Y, C242S, C242F, G244S, G244C, G244D, G245S, G245R, G245C, G245D, G245A, G245V, G245S, M246V, M246K, M246R, M246I, N247I, R248W, R248G, R248Q, R249G, R249W, R249T, R249M, P250L, I251N, L252P, I254S, I255F, I255N, I255S, L257Q, L257P, E258K, E258Q, D259Y, S261T, G262D, G262V, L265P, G266R, G266E, G266V, R267W, R267Q, R267P, E271K, V272M, V272L, R273S, R273G, R273C, R273H, R273P, R273L, V274F, V274D, V274A, V274G, V274L, C275Y, C275S, C275F, A276P, C277F, P278T, P278A, P278S, P278H, P278R, P278L, G279E, R280G, R280K, R280T, R280I, R280S, D281N, D281H, D281Y, D281G, D281E, R282G, R282W, R282Q, R282P, E285K, E285V, E286G, E286V, E286K, K320N, L330R, G334V, R337QR337L, A347T, L348F, T377P.

If the mutation is in JAK2, the mutation is advantageously selected from the group consisting of N533D, N533Y, N533S, H538R, K539E, K539L, I540T, I540V, V617F, R683S, R683G, del/ins537-539L, del/ins538-539L, del/ins540-543MK, del/ins540-544MK, del/ins541-543K, del542-543, del543-544 and ins 11546-547.

If the mutation is in SF3B1, the mutation is advantageously selected from the group consisting of G347V, R387W, R387Q, E592K, E622D, Y623C, R625L, R625C, H662Q, H662D, K666N, K666T, K666E, K666R, K700E, V701F, A708T, G740R, G740E, A744P, D781G and E783K.

Aging is associated with a large increase in the prevalence of atherosclerosis and cancer. Applicants recently analyzed whole exome sequencing data from over 17,000 individuals who were unselected for hematological phenotypes and found that at least 10% of humans age 70 or older harbor a mutation in a known cancer-causing gene in their blood cells (Jaiswal et al., NEJM 2014). Surprisingly, the presence of these mutations was associated with an increased risk of myocardial infarction (hazard ratio [HR]=2.0) and ischemic stroke (HR=2.6) in −3,000 individuals for whom long-term follow-up information was available. It is unknown if somatic mutations that cause clonal expansion of hematopoietic stem cells also affect the function of differentiated blood cells such as macrophages, which are considered to be important mediators of atherosclerosis. Preliminary mouse data indicates that at least one of these mutations (TET2) does indeed directly alter blood cell function to lead to accelerated atherosclerosis. This application seeks to definitively establish whether these somatic mutations in blood cells are causally linked to atherosclerosis. Applicants expand the initial findings by assessing whether mice bearing mutations in DNMT3A, TET2, or JAK2 in their blood cells have accelerated atherosclerosis. Applicants probe the molecular mechanism of accelerated atherosclerosis in these mice. Applicants look for alterations in expression of liver X receptor (LXR), peroxisome proliferator activated receptor gamma (PPARG), and lipopolysaccharide (LPS) target genes in mutant macrophages using RNA-seq, DNase footprinting, and bisulfite sequencing. Applicants identify and validate therapeutic targets in macrophages with these somatic mutations using drug screens in newly created cell-line models.

Atherosclerosis is the leading cause of death in the United States; however, little is known about non-lipid risk factors in humans. This application relates to a mechanism behind the proposed causal association between these somatic mutations in blood cells and atherosclerosis.

Cancer is thought to arise via the stepwise acquisition of genetic or epigenetic mutations that transform a normal cell (P. C. Nowell, Science 194, 23-28 (1976), J. S. Welch et al., Cell 150, 264-278 (2012)). For most hematological cancers, the genes that are frequently mutated are now known (R. Bejar et al, N Engl J Med 364, 2496-2506 (2011, N. Cancer Genome Atlas Research, Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med 368, 2059-2074 (2013), E. Papaemmanuil et al, Blood 122, 3616-3627; quiz 3699 (2013), T. Haferlach et al, Leukemia 28, 241-247 (2014)). Applicants hypothesized that these mutations would be detectable in the blood cells of some individuals not known to have hematological disorders, and that the presence of these mutations would represent a pre-malignant state. To test this hypothesis, Applicants examined whole exome sequencing data from large cohorts who were unselected for hematological phenotypes (S. Jaiswal et al, N Engl J Med 371, 2488-2498 (2014), G. Genovese et al, N Engl J Med 371, 2477-2487 (2014), M. Xie et al., Nat Med 20, 1472-1478 (2014)). The DNA source for these exomes was peripheral blood cells. Applicants found that −10% of individuals over the age of 70 harbored a detectable mutation in their blood cells (FIG. 1). The majority of the mutations could be accounted for by loss-of-function mutations in 3 genes, DNMT3A, TET2, and ASXL1, while activating mutations in JAK2 were the fifth most common mutation. These mutations are common in myeloid malignancies. Compared to those without mutations, persons who harbored these mutations were over 10 times more likely to develop a hematologic malignancy over the next several years, confirming that this condition is a bona fide pre-malignant entity (D. P. Steensma et al., Blood 126, 9-16 (2015)).

Surprisingly, the presence of these mutations was also associated with an increased risk of mortality that could not be explained by hematological malignancy alone. Applicants found that those with mutations had a higher rate of having a fatal myocardial infarction (MI) or ischemic stroke. When Applicants looked at risk of developing incident coronary heart disease or ischemic stroke, Applicants found that the presence of mutations was an even stronger risk factor than smoking, hypertension, or elevated cholesterol (FIG. 5).

To replicate these findings, Applicants examined whole exome sequencing data from two large case-control cohorts designed to study early-onset MI. The cases (n=4,405) were individuals from Italy or Pakistan who had blood cells collected for DNA sequencing at the time of presentation of MI, and controls (n=3,006) were age-matched healthy individuals from the same populations. Applicants found an even greater risk associated with mutations in these cohorts; the presence of a mutation was over 8 times more likely in the MI cases as compared to age-matched controls for those 40 or younger, and 3 times more likely in those age 41-50 (FIG. 16A). DNMT3A, TET2, ASXL1, and JAK2 were the most frequently mutated genes, and were strongly enriched in the cases (FIG. 16B).

These genetic studies have firmly established a statistical link between the presence of these mutations and atherosclerotic disease, but alone cannot establish causality. It is possible that this association is merely a correlation, and that the presence of mutations is a molecular marker of aging. However, a few observations suggest a causal relationship. First, Applicants' human genetic data shows a dose-response relationship between the size of the mutant clone and risk of atherosclerotic disease. There is a greatly increased risk of incident coronary heart disease (HR=4.4) in those with a mutant allele fraction of >0.10 (meaning >20% of the blood cells harbor the mutation, including the macrophages in vessel walls), while those with clones smaller than this size had no increased risk. Second, there is a large body of evidence to suggest that macrophages are important in the development of atherosclerosis (K. J. Moore et al., Nat Rev Immunol 13, 709-721 (2013)). They are the most prevalent cell type within vessel wall lesions, and are critical for reverse cholesterol transport (RCT), the process by which excess lipid is exported from tissues for clearance by the liver (M. Cuchel et al, Circulation 113, 2548-2555 (2006)). Third, inflammation and alterations in blood cell parameters are linked to adverse cardiovascular outcomes in epidemiological studies (R. Ross, N Engl J Med 340, 115-126 (1999), P. Libby, Nature 420, 868-874 (2002), P. Libby et al, Am J Med 116 Suppl 6A, 9S-16S (2004), P. M. Ridker et al, N Engl J Med 347, 1557-1565 (2002), M. Tonelli et al, Circulation 117, 163-168 (2008), M. Madjid et al, J Am Coll Cardiol 44, 1945-1956 (2004)).

Wild-type mice have much lower baseline cholesterol and triglyceride levels than humans in modern societies. Therefore, a standard experimental model is to use mice lacking low-density lipoprotein receptor {Ldlr-I-) (E. Maganto-Garcia et al., Current protocols in immunology/edited by John E. Coligan . . . [et al.] Chapter 15, Unit 15 24 11-23 (2012)). These mice rapidly develop atherosclerosis when fed a high cholesterol diet. By transplanting bone marrow into Ldlr-I- mice from a genetically defined mouse strain, one can test the effect of a genetic perturbation in hematopoietic cells on the development of atherosclerosis.

In general, studies of experimental atherosclerosis have revealed two relevant processes that can be influenced by macrophages: 1) reverse cholesterol transport, and 2) the local inflammatory milieu. The importance of macrophage reverse cholesterol transport is exemplified by mice in which both liver X receptor genes have been knocked out in the hematopoietic compartment (LXRαβ-I-) (R. K. Tangirala et al., Proceedings of the National Academy of Sciences of the United States of America 99, 11896-11901 (2002)). Recipients transplanted with marrow from these mice develop accelerated atherosclerosis and have a lipid accumulation phenotype in several tissues. LXRs are a class of nuclear receptors expressed in macrophages, hepatocytes, and intestinal epithelium that activate transcription of genes involved in cholesterol transport and lipid metabolism when bound by ligand. In the absence of LXR mediated transcription, macrophages do not efficiently upregulate expression of the cholesterol transporters ABCA1 and ABCG1. This leads to a defect in exporting cholesterol to acceptor molecules such as high-density lipoprotein (HDL) and Apo-AI, ultimately resulting in foam cell formation in tissues such as the vessel wall, skin, and spleen.

The role of inflammation in atherosclerosis has also been well studied. Mice that are deficient in toll-like receptor (TLR)-2 (A. E. Mullick et al, J Clin Invest 115, 3149-3156 (2005)), TLR4 (K. S. Michelsen et al., Proceedings of the National Academy of Sciences of the United States of America 101, 10679-10684 (2004)), or inflammasome (P. Duewell et al, Nature 464, 1357-1361 (2010)) signaling have reduced atherosclerosis, whereas mice that lack the inflammatory repressor BCL6 in macrophages have accelerated atherosclerosis (G. D. Barish et al., Cell Metab 15, 554-562 (2012)). Macrophage mediated inflammation results in recruitment of other immune cells and intimal hyperplasia, resulting in increased lesion size and instability. Additional evidence indicates that inflammatory signaling may also directly inhibit macrophage RCT (A. Castrillo et al, Mol Cell 12, 805-816 (2003)). Finally, activation of another class of nuclear receptors, PPARs, ameliorates atherosclerosis by increasing macrophage RCT via LXR activation (A. Chawla et al., Mol Cell 7, 161-171 (2001)), as well as by attenuating inflammation via induction of the transcriptional repressors BCL6, NCOR1, and NCOR2 (G. Pascual et al., Nature 437, 759-763 (2005), A. Chawla, Circ Res 106, 1559-1569 (2010)).

To test the hypothesis that mutations in blood cells are causally linked to atherosclerosis, Applicants turned to defined mouse models. Applicants crossed Tet2^(fl/fl) mice to Vavl-Cre mice; the resultant Tet2^(fl/fl), Vav1-Cre mice have exon 3 of TET2 deleted, leading to complete loss of Tet2 function in all of their hematopoietic cells. Previous studies have demonstrated that hematopoietic stem cells (HSCs) from these mice have a differentiation defect that results in a progressive increase in HSC frequency and consequent clonal expansion of the mutant cells in a competitive transplant setting (K. Moran-Crusio et al., Cancer cell 20, 11-24 (2011)). Thus, this model system recapitulates the clonal advantage of TET2 mutant hematopoietic cells seen in humans. Applicants then transplanted bone marrow from these mice, or control Vavl-Cxo, mice, into Ldlr−/− recipient mice and initiated high cholesterol diet. Tissues were then examined at several time points. The results from these preliminary experiments are clear; mice that received Tet2-I- bone marrow had not only increased lesion size in the aortic root (FIG. 17 A), but also striking numbers of lipid-laden macrophages in the spleen, skin, and peritoneal fluid (FIG. 17B). This phenotype is remarkably similar to the one described for mice transplanted with ΣXKαβ-I- marrow (R. K. Tangirala et al., Proceedings of the National Academy of Sciences of the United States of America 99, 11896-11901 (2002)). In summary, these results point to a marked deficit of reverse cholesterol transport in macrophages that lack TET2, of which one manifestation is accelerated atherosclerosis.

The present invention relates to determining the effect of mutations in TET2, DNMT3A, and JAK2 on atherosclerosis development in vivo. Applicants has already generated or obtained models for DNMT3A loss-of-function (Dnmt3a^(fl/fl) mice) (S. Nguyen et al., Dev Dyn 236, 1663-1676 (2007)) and JAK2 gain-of-function (floxed Jak2 V617F knock-in heterozygous mice) (A. Mullally et al., Cancer cell 17, 584-596 (2010)). Unfortunately, there are no publicly available mouse models of ASXL1 that mimic the mutations seen in humans, which are truncating mutations in exon 12. Applicants hypothesize that introducing these mutations into hematopoietic cells lead to accelerated atherosclerosis using the Ldlr−/− transplant system described above. Applicants perform the transplants described above and analyze mice after 5, 9, and 14 weeks on high-cholesterol (1.25%) diet. Lesion size and cellular composition, as well serum lipid profiles are measured. Applicants also examine lipid content of macrophages in the spleen, lung, liver, skin, intestines, and peritoneal fluid to determine if there is a global defect in macrophage RCT. It is important to note that the mice are not expected to develop a leukemic phenotype within this time frame. Applicants also determine whether other cells within the hematopoietic compartment besides macrophages can contribute to the phenotype. Applicants cross the TET2, DNMT3A, and JAK2 mutant mice to mice that have CD2-Cre (lymphoid specific) or PF4-Cre (platelet specific), then perform transplants into Ldlr-I- mice and determine the extent of atherosclerosis in each model after 9 weeks on diet. Adgrel-Cre mice, which express Cre in mature macrophages only, are used to test the hypothesis that the mutations do not necessarily need to occur in stem cells to exert a phenotype.

It is possible that macrophages are the cells responsible for the phenotype, but that the mutations must occur in stem cells in order to have a functional effect due to altered differentiation. If the mutant strains crossed with Adgrel-Cre do not have a phenotype, Applicants isolate monocyte precursors (J. Hettinger et al., Nat Immunol 14, 821-830 (2013)) from mutant Vavl-Cre mice and transplant these into recipient Ldlr-I- mice to test the hypothesis that monocytes/macrophages alone can recapitulate the phenotype.

The present application also relates to determining the mechanism by which mutations in TET2, DNMT3A, and JAK2 lead to altered macrophage function and accelerated atherosclerosis. As detailed above, much experimental evidence indicates that macrophage function has a profound effect on the development of atherosclerosis. Applicants hypothesize that mutations in TET2, DNMT3A, and JAK2 lead to accelerated atherosclerosis by inhibiting macrophage reverse cholesterol transport, increasing macrophage-mediated inflammation, or both. Applicants characterize the transcriptional response of mutant macrophages to induction of RCT and inflammation by agonists of LXRs, PPARG, and TLR4.

To ensure robustness of results, Applicants utilize 2 types of primary macrophages: thioglycollate-induced peritoneal macrophages and bone-marrow derived macrophages grown in vitro in the presence of cytokines. The specific agonists to be used are GW3965 (LXR alpha/beta agonist), pioglitazone (PPARG agonist), and LPS (TLR4 agonist). Gene expression are assessed by RNA-sequencing. Gene set enrichment analysis are used to determine the extent of transcriptional changes for various classes of genes by comparing expression in mutant/wild-type cells that have been treated to mutant/wild-type cells that are not treated. For these experiments, gene expression are measured at two time-points after exposure to agonists to also assess the kinetics of the transcriptional response.

Applicants are uncovering the mechanistic link between the specific mutations and alterations in gene expression in response to the agonists listed above. Applicants perform DNase footprinting and bisulfite sequencing to molecularly determine the how mutant macrophages are altered in response to the specific agonists. Peritoneal macrophages are used in these experiments because they represent an in vivo baseline epigenetic state. DNase footprinting provides a powerful method to infer transcription factor binding in a genome-wide manner by identifying DNA sequence motifs that are protein bound. Bisulfite sequencing provides single base resolution of cytosine methylation. As TET2 and DNMT3A are enzymes that alter DNA methylation, it is likely that perturbing their function result in an abnormal epigenetic state. For example, TET2 converts 5-methylcytosine to 5-hydroxymethylcytosine, which ultimately leads to de-methylation. As methylation at promoters and enhancers anti-correlates with gene expression and transcription factor binding, Applicants hypothesize that loss of TET2 function results in abnormal methylation of cis-regulatory elements for LXR/PPARG targets, reduced binding of transcription factors at these elements, and ultimately attenuated expression of the target genes. DNMT3A, a de novo DNA methyltransferase, has a function that opposes that of TET2. Applicants hypothesize that de novo methylation at cis-regulatory elements of proinflammatory genes is necessary to attenuate an inflammatory response in macrophages, and that this regulatory check is lacking in DNMT3A null macrophages. Unlike TET2 and DNMT3A, JAK2 is unique in that it is an activator of STAT signaling, not an epigenetic regulator. STAT transcription factors are known to activate a variety of genes involved in the immune response. Applicants hypothesize that constitutive STAT signaling in the setting of JAK2 activating mutations results in a stronger/more protracted pro-inflammatory phenotype in mutant macrophages, which are determined by assessing the transcriptional response to LPS. Applicants are performing functional assays to assess the physiological effect of the mutations on macrophage activity. Applicants assess cholesterol efflux, cholesterol uptake, efferocytosis, LPS tolerance, and M1/M2 polarization in mutant and wild-type macrophages in response to the specific agonists. Applicants hypothesize that one or more of these processes are altered due to the mutations.

While DNase footprinting is a powerful technique, it relies upon knowing DNA motifs for specific transcription factors to accurately assign binding. Furthermore, enhancers are inferred from transcription factor binding, rather than by assessing chromatin marks. Therefore, Applicants also consider chromatin immunoprecipitation sequencing for LXR alpha and beta, PPARG, RelA, RelB, c-REL, STAT3, and STAT5, as well as the canonical histone marks for enhancers (H3K4mel, H3K4me3, H3K27ac, H3K27me3). Finally, Applicants also consider assessment of 5-hydroxymethylcytosine as technology to detect this mark continues to improve.

The present invention also relates to identifying molecules and pathways that can reverse the pro-atherogenic phenotype of mutant macrophages. Applicants are identifying therapeutic targets for macrophages that have mutations in TET2, DNMT3A, or JAK2. Applicants hypothesize that specific molecules or pathways can be targeted to reverse some aspects of the pro-atherogenic phenotype of mutant macrophages. Applicants are testing whether existing agonists for LXR and PPARG can reverse the phenotype in the bone marrow transplant models. Mice are placed on drug after 8 weeks on high cholesterol diet, and plaque size are compared between treated/untreated and mutant/wild-type mice after an additional 12 weeks on diet. While these approaches may prove efficacious in mice, current LXR and PPARG agonists are not considered front-line drugs in humans because of side-effects and the potential for serious adverse events. Thus, Applicants are identifying novel targets that can reverse the phenotype specifically in mutant macrophages. Applicants utilize THP-1 cells, a monocytic leukemia cell line that retains the ability differentiate into macrophages and is responsive to LXR and PPARG agonists. Frameshift mutations in TET2 and DNMT3A are created by CRISP-Cas9, while JAK2 V617F are introduced lentivirally. Applicants also introduce artificial reporters that activate fluorescence by LXR agonists, PPARG agonists, or LPS (via NF-κB activation). Applicants are screening the cells using a highly targeted drug library of 481 molecules that affect distinct cellular pathways and processes (A. Basu et al, Cell 154, 1151-1161 (2013)). The read-out is an image-based assessment of fluorescence intensity from the reporter. The aim is to identify molecules that preferentially activate or repress the reporter in mutant, but not wild-type macrophages. Applicants are also testing individual compounds that show desired activity in the mouse bone marrow transplant model, as well as in in vitro assays in primary macrophages.

The small molecule approach described above may not yield any viable candidates for a variety of reasons. In this case, a second screen is performed using a CRISPR-Cas9/small-guide RNA system to inactivate all protein-coding genes. Briefly, THP-1 cells with reporter are transduced with Cas9 and a pooled library of small guide RNA molecules. After inducing macrophage differentiation, Applicants identify cells that activate or repress the reporter in response to agonists. DNA sequencing is used to determine which guides are present, and the positive hits are further validated to ensure that they preferentially affect mutant cells.

With respect to general information on CRISPR-Cas Systems, components thereof, and delivery of such components, including methods, materials, delivery vehicles, vectors, particles, AAV, and making and using thereof, including as to amounts and formulations, all useful in the practice of the instant invention, reference is made to: U.S. Pat. Nos. 8,697,359, 8,771,945, 8,795,965, 8,865,406, 8,871,445, 8,889,356, 8,889,418, 8,895,308, 8,906,616, 8,932,814, 8,945,839, 8,993,233 and 8,999,641; US Patent Publications US 2014-0310830 (U.S. application Ser. No. 14/105,031), US 2014-0287938 A1 (U.S. application Ser. No. 14/213,991), US 2014-0273234 A1 (U.S. application Ser. No. 14/293,674), US2014-0273232 A1 (U.S. application Ser. No. 14/290,575), US 2014-0273231 (U.S. application Ser. No. 14/259,420), US 2014-0256046 A1 (U.S. application Ser. No. 14/226,274), US 2014-0248702 A1 (U.S. application Ser. No. 14/258,458), US 2014-0242700 A1 (U.S. application Ser. No. 14/222,930), US 2014-0242699 A1 (U.S. application Ser. No. 14/183,512), US 2014-0242664 A1 (U.S. application Ser. No. 14/104,990), US 2014-0234972 A1 (U.S. application Ser. No. 14/183,471), US 2014-0227787 A1 (U.S. application Ser. No. 14/256,912), US 2014-0189896 A1 (U.S. application Ser. No. 14/105,035), US 2014-0186958 (U.S. application Ser. No. 14/105,017), US 2014-0186919 A1 (U.S. application Ser. No. 14/104,977), US 2014-0186843 A1 (U.S. application Ser. No. 14/104,900), US 2014-0179770 A1 (U.S. application Ser. No. 14/104,837) and US 2014-0179006 A1 (U.S. application Ser. No. 14/183,486), US 2014-0170753 (U.S. application Ser. No. 14/183,429); US 2015-0184139 (U.S. application Ser. No. 14/324,960); Ser. No. 14/054,414 European Patent Applications EP 2 771 468 (EP13818570.7), EP 2 764 103 (EP13824232.6), and EP 2 784 162 (EP14170383.5); and PCT Patent Publications WO 2014/093661 (PCT/US2013/074743), WO 2014/093694 (PCT/US2013/074790), WO 2014/093595 (PCT/US2013/074611), WO 2014/093718 (PCT/US2013/074825), wo 2014/093709 (PCT/US2013/074812), wo 2014/093622 (PCT/US2013/074667), wo 2014/093635 (PCT/US2013/074691), wo 2014/093655 (PCT/US2013/074736), wo 2014/093712 (PCT/US2013/074819), wo 2014/093701 (PCT/US2013/074800), wo 2014/018423 (PCT/US2013/051418), wo 2014/204723 (PCT/US2014/041790), wo 2014/204724 (PCT/US2014/041800), wo 2014/204725 (PCT/US2014/041803), wo 2014/204726 (PCT/US2014/041804), wo 2014/204727 (PCT/US2014/041806), wo 2014/204728 (PCT/US2014/041808), wo 2014/204729 (PCT/US2014/041809), wo 2015/089351 (PCT/US2014/069897), wo 2015/089354 (PCT/US2014/069902), WO 2015/089364 (PCT/US2014/069925), WO 2015/089427 (PCT/US2014/070068), WO 2015/089462 (PCT/US2014/070127), WO 2015/089419 (PCT/US2014/070057), WO 2015/089465 (PCT/US2014/070135), WO 2015/089486 (PCT/US2014/070175), PCT/US2015/051691, PCT/US2015/051830. Reference is made to PCT application designating, inter alia, the United States, application No. PCT/US 14/41806, filed Jun. 10, 2014. Reference is made to PCT application designating, inter alia, the United States, application No. PCT/US14/41806, filed Jun. 10, 2014.

Each of these patents, patent publications, and applications, and all documents cited therein or during their prosecution (“appln cited documents”) and all documents cited or referenced in the appln cited documents, together with any instructions, descriptions, product specifications, and product sheets for any products mentioned therein or in any document therein and incorporated by reference herein, are hereby incorporated herein by reference, and may be employed in the practice of the invention. All documents (e.g., these patents, patent publications and applications and the appln cited documents) are incorporated herein by reference to the same extent as if each individual document was specifically and individually indicated to be incorporated by reference.

Also with respect to general information on CRISPR-Cas Systems, mention is made of the following (also hereby incorporated herein by reference):

-   Multiplex genome engineering using CRISPR/Cas systems. Cong, L.,     Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., Hsu, P. D.,     Wu, X., Jiang, W., Marraffmi, L. A., & Zhang, F. Science February     15; 339(6121):819-23 (2013); -   RNA-guided editing of bacterial genomes using CRISPR-Cas systems.     Jiang W., Bikard D., Cox D., Zhang F, Marraffmi L A. Nat Biotechnol     March; 31(3):233-9 (2013); One-Step Generation of Mice Carrying     Mutations in Multiple Genes by CRISPR/Cas-Mediated Genome     Engineering. Wang F L, Yang F L, Shivalila C S., Dawlaty M M., Cheng     A W., Zhang F., Jaenisch R. Cell May 9; 153(4):910-8 (2013); -   Optical control of mammalian endogenous transcription and epigenetic     states. Konermann S, Brigham M D, Trevino A E, Hsu P D, Heidenreich     M, Cong L, Piatt R J, Scott D A, Church G M, Zhang F. Nature. 2013     Aug. 22; 500(7463):472-6. doi: 10.1038/Naturel2466. Epub 2013 Aug.     23; -   Double Nicking by RNA-Guided CRISPR Cas9 for Enhanced Genome Editing     Specificity. Ran, F A., Hsu, P D., Lin, C Y., Gootenberg, J S.,     Konermann, S., Trevino, A E., Scott, D A., Inoue, A., Matoba, S.,     Zhang, Y., & Zhang, F. Cell August 28. pii: S0092-8674(13)01015-5.     (2013; -   DNA targeting specificity of RNA-guided Cas9 nucleases. Hsu, P.,     Scott, D., Weinstein, J., Ran, F A., Konermann, S., Agarwala, V.,     Li, Y., Fine, E., Wu, X., Shalem, O., Cradick, T J., Marraffini, L     A., Bao, G., & Zhang, F. Nat Biotechnol doi: 10.1038/nbt.2647     (2013); -   Genome engineering using the CRISPR-Cas9 system. Ran, F A., Hsu, P     D., Wright, J., Agarwala, V., Scott, D A., Zhang, F. Nature     Protocols November; 8(11):2281-308. (2013); Genome-Scale CRISPR-Cas9     Knockout Screening in Human Cells. Shalem, O., Sanjana, N E.,     Hartenian, E., Shi, X., Scott, D A., Mikkelson, T., Heckl, D.,     Ebert, B L., Root, D E., Doench, J G., Zhang, F. Science     December 12. (2013). [Epub ahead of print]; -   Crystal structure of cas9 in complex with guide RNA and target DNA.     Nishimasu, H., Ran, F A., Hsu, P D., Konermann, S., Shehata, S I.,     Dohmae, N., Ishitani, R., Zhang, F., Nureki, O. Ce// Feb. 27.     (2014). 156(5):935-49; -   Genome-wide binding of the CRISPR endonuclease Cas9 in mammalian     cells. Wu X., Scott D A., Kriz A J., Chiu A C, Hsu P D., Dadon D B.,     Cheng A W., Trevino A E., Konermann S., Chen S., Jaenisch R., Zhang     F., Sharp P A. Nat Biotechnol. (2014) April 20. doi:     10.1038/nbt.2889, -   CRISPR-Cas9 Knockin Mice for Genome Editing and Cancer Modeling,     Piatt et al., Cell 159(2): 440-455 (2014) DOI:     10.1016/j.cell.2014.09.014, -   Development and Applications of CRISPR-Cas9 for Genome Engineering,     Hsu et al, Cell 157, 1262-1278 (Jun. 5, 2014) (Hsu 2014), -   Genetic screens in human cells using the CRISPR/Cas9 system, Wang et     al., Science. 2014 Jan. 3; 343(6166): 80-84. doi:     10.1126/science.1246981, -   Rational design of highly active sgRNAs for CRISPR-Cas9-mediated     gene inactivation, Doench et al, Nature Biotechnology published     online 3 Sep. 2014; doi: 10.1038/nbt.3026, and -   In vivo interrogation of gene function in the mammalian brain using     CRISPR-Cas9, Swiech et al, Nature Biotechnology; published online 19     Oct. 2014; doi: 10.1038/nbt.3055. -   Genome-scale transcriptional activation by an engineered CRISPR-Cas9     complex, Konermann S, Brigham M D, Trevino A E, Joung J, Abudayyeh     00, Barcena C, Hsu P D, Habib N, Gootenberg J S, Nishimasu H, Nureki     O, Zhang F., Nature. January 29; 517(7536):583-8 (2015). -   A split-Cas9 architecture for inducible genome editing and     transcription modulation, Zetsche B, Volz S E, Zhang F., (published     online 2 Feb. 2015) Nat Biotechnol. February; 33(2): 139-42 (2015); -   Genome-wide CRISPR Screen in a Mouse Model of Tumor Growth and     Metastasis, Chen S, Sanjana N E, Zheng K, Shalem O, Lee K, Shi X,     Scott D A, Song J, Pan J Q, Weissleder R, Lee H, Zhang F, Sharp P A.     Cell 160, 1246-1260, Mar. 12, 2015 (multiplex screen in mouse), and -   In vivo genome editing using Staphylococcus aureus Cas9, Ran F A,     Cong L, Yan W X, Scott D A, Gootenberg J S, Kriz A J, Zetsche B,     Shalem O, Wu X, Makarova K S, Koonin E V, Sharp P A, Zhang F.,     (published online 1 Apr. 2015), Nature. April 9; 520(7546): 186-91     (2015). -   High-throughput functional genomics using CRISPR-Cas9, Shalem et     al., Nature Reviews Genetics 16, 299-311 (May 2015). -   Sequence determinants of improved CRISPR sgRNA design, Xu et al.,     Genome Research 25, 1147-1157 (August 2015). -   A Genome-wide CRISPR Screen in Primary Immune Cells to Dissect     Regulatory Networks, Parnas et al, Cell 162, 675-686 (Jul. 30,     2015). -   CRISPR/Cas9 cleavage of viral DNA efficiently suppresses hepatitis B     virus, Ramanan et al, Scientific Reports 5: 10833. doi:     10.1038/srepl0833 (Jun. 2, 2015). Crystal Structure of     Staphylococcus aureus Cas9, Nishimasu et al., Cell 162, 1113-1126     (Aug. 27, 2015). -   BCL11A enhancer dissection by Cas9-mediated in situ saturating     mutagenesis, Canver et al, Nature 527(7577): 192-7 (Nov. 12, 2015)     doi: 10.1038/naturel5521. Epub 2015 Sep. 16. -   Cpfl Is a Single RNA-Guided Endonuclease of a Class 2 CRISPR-Cas     System, Zetsche et al, Cell 163, 759-71 (Sep. 25, 2015). -   Discovery and Functional Characterization of Diverse Class 2     CRISPR-Cas Systems, Shmakov et al, Molecular Cell, 60(3), 385-397     doi: 10.1016/j.molcel.2015.10.008 Epub Oct. 22, 2015.

each of which is incorporated herein by reference, and discussed briefly below:

-   -   Cong et al. engineered type II CRISPR/Cas systems for use in         eukaryotic cells based on both Streptococcus thermophilus Cas9         and also Streptoccocus pyogenes Cas9 and demonstrated that Cas9         nucleases can be directed by short RNAs to induce precise         cleavage of DNA in human and mouse cells. Their study further         showed that Cas9 as converted into a nicking enzyme can be used         to facilitate homology-directed repair in eukaryotic cells with         minimal mutagenic activity. Additionally, their study         demonstrated that multiple guide sequences can be encoded into a         single CRISPR array to enable simultaneous editing of several at         endogenous genomic loci sites within the mammalian genome,         demonstrating easy programmability and wide applicability of the         RNA-guided nuclease technology. This ability to use RNA to         program sequence specific DNA cleavage in cells defined a new         class of genome engineering tools. These studies further showed         that other CRISPR loci are likely to be transplantable into         mammalian cells and can also mediate mammalian genome cleavage.         Importantly, it can be envisaged that several aspects of the         CRISPR/Cas system can be further improved to increase its         efficiency and versatility.     -   Jiang et al. used the clustered, regularly interspaced, short         palindromic repeats (CRISPR)-associated Cas9 endonuclease         complexed with dual-RNAs to introduce precise mutations in the         genomes of Streptococcus pneumoniae and Escherichia coli. The         approach relied on dual-RNA:Cas9-directed cleavage at the         targeted genomic site to kill unmutated cells and circumvents         the need for selectable markers or counter-selection systems.         The study reported reprogramming dual-RNA:Cas9 specificity by         changing the sequence of short CRISPR RNA (crRNA) to make         single- and multinucleotide changes carried on editing         templates. The study showed that simultaneous use of two crRNAs         enabled multiplex mutagenesis. Furthermore, when the approach         was used in combination with recombineering, in S. pneumoniae,         nearly 100% of cells that were     -   recovered using the described approach contained the desired         mutation, and in E. coli, 65% that were recovered contained the         mutation.     -   Wang et al. (2013) used the CRISPR/Cas system for the one-step         generation of mice carrying mutations in multiple genes which         were traditionally generated in multiple steps by sequential         recombination in embryonic stem cells and/or time-consuming         intercrossing of mice with a single mutation. The CRISPR/Cas         system will greatly accelerate the in vivo study of functionally         redundant genes and of epistatic gene interactions.     -   Konermann et al. addressed the need in the art for versatile and         robust technologies that enable optical and chemical modulation         of DNA-binding domains based CRISPR Cas9 enzyme and also         Transcriptional Activator Like Effectors.     -   Ran et al. (2013-A) described an approach that combined a Cas9         nickase mutant with paired guide RNAs to introduce targeted         double-strand breaks. This addresses the issue of the Cas9         nuclease from the microbial CRISPR-Cas system being targeted to         specific genomic loci by a guide sequence, which can tolerate         certain mismatches to the DNA target and thereby promote         undesired off-target mutagenesis. Because individual nicks in         the genome are repaired with high fidelity, simultaneous nicking         via appropriately offset guide RNAs is required for         double-stranded breaks and extends the number of specifically         recognized bases for target cleavage. The authors demonstrated         that using paired nicking can reduce off-target activity by 50-         to 1,500-fold in cell lines and to facilitate gene knockout in         mouse zygotes without sacrificing on-target cleavage efficiency.         This versatile strategy enables a wide variety of genome editing         applications that require high specificity.     -   Hsu et al. (2013) characterized SpCas9 targeting specificity in         human cells to inform the selection of target sites and avoid         off-target effects. The study evaluated >700 guide RNA variants         and SpCas9-induced indel mutation levels at >100 predicted         genomic off-target loci in 293T and 293FT cells. The authors         that SpCas9 tolerates mismatches between guide RNA and target         DNA at different positions in a sequence-dependent manner,         sensitive to the number, position and distribution of         mismatches. The authors further showed that SpCas9-mediated         cleavage is unaffected by DNA methylation and that the dosage of         SpCas9 and sgRNA can be titrated to minimize off-target         modification.     -   Additionally, to facilitate mammalian genome engineering         applications, the authors reported providing a web-based         software tool to guide the selection and validation of target         sequences as well as off-target analyses.     -   Ran et al. (2013-B) described a set of tools for Cas9-mediated         genome editing via nonhomologous end joining (NHEJ) or         homology-directed repair (HDR) in mammalian cells, as well as         generation of modified cell lines for downstream functional         studies. To minimize off-target cleavage, the authors further         described a double-nicking strategy using the Cas9 nickase         mutant with paired guide RNAs. The protocol provided by the         authors experimentally derived guidelines for the selection of         target sites, evaluation of cleavage efficiency and analysis of         off-target activity. The studies showed that beginning with         target design, gene modifications can be achieved within as         little as 1-2 weeks, and modified clonal cell lines can be         derived within 2-3 weeks.     -   Shalem et al. described a new way to interrogate gene function         on a genome-wide scale. Their studies showed that delivery of a         genome-scale CRISPR-Cas9 knockout (GeCKO) library targeted         18,080 genes with 64,751 unique guide sequences enabled both         negative and positive selection screening in human cells. First,         the authors showed use of the GeCKO library to identify genes         essential for cell viability in cancer and pluripotent stem         cells. Next, in a melanoma model, the authors screened for genes         whose loss is involved in resistance to vemurafenib, a         therapeutic that inhibits mutant protein kinase BRAF. Their         studies showed that the highest-ranking candidates included         previously validated genes NF1 and MED 12 as well as novel hits         NF2, CUL3, TADA2B, and TADA1. The authors observed a high level         of consistency between independent guide RNAs targeting the same         gene and a high rate of hit confirmation, and thus demonstrated         the promise of genome-scale screening with Cas9.     -   Nishimasu et al. reported the crystal structure of Streptococcus         pyogenes Cas9 in complex with sgRNA and its target DNA at 2.5 A°         resolution. The structure revealed a bilobed architecture         composed of target recognition and nuclease lobes, accommodating         the sgRNA:DNA heteroduplex in a positively charged groove at         their interface. Whereas the recognition lobe is essential for         binding sgRNA and DNA, the nuclease lobe contains the FiNH and         RuvC nuclease domains, which are properly positioned for         cleavage of the complementary and non-complementary strands of         the target DNA, respectively. The     -   nuclease lobe also contains a carboxyl-terminal domain         responsible for the interaction with the protospacer adjacent         motif (PAM). This high-resolution structure and accompanying         functional analyses have revealed the molecular mechanism of         RNA-guided DNA targeting by Cas9, thus paving the way for the         rational design of new, versatile genome-editing technologies.     -   Wu et al. mapped genome-wide binding sites of a catalytically         inactive Cas9 (dCas9) from Streptococcus pyogenes loaded with         single guide RNAs (sgRNAs) in mouse embryonic stem cells         (mESCs). The authors showed that each of the four sgRNAs tested         targets dCas9 to between tens and thousands of genomic sites,         frequently characterized by a 5-nucleotide seed region in the         sgRNA and an NGG protospacer adjacent motif (PAM). Chromatin         inaccessibility decreases dCas9 binding to other sites with         matching seed sequences; thus 70% of off-target sites are         associated with genes. The authors showed that targeted         sequencing of 295 dCas9 binding sites in mESCs transfected with         catalytically active Cas9 identified only one site mutated above         background levels. The authors proposed a two-state model for         Cas9 binding and cleavage, in which a seed match triggers         binding but extensive pairing with target DNA is required for         cleavage. Piatt et al. established a Cre-dependent Cas9 knockin         mouse. The authors demonstrated in vivo as well as ex vivo         genome editing using adeno-associated virus (AAV)-, lentivirus-,         or particle-mediated delivery of guide RNA in neurons, immune         cells, and endothelial cells.     -   Hsu et al. (2014) is a review article that discusses generally         CRISPR-Cas9 history from yogurt to genome editing, including         genetic screening of cells.     -   Wang et al. (2014) relates to a pooled, loss-of-function genetic         screening approach suitable for both positive and negative         selection that uses a genome-scale lentiviral single guide RNA         (sgRNA) library.     -   Doench et al. created a pool of sgRNAs, tiling across all         possible target sites of a panel of six endogenous mouse and         three endogenous human genes and quantitatively assessed their         ability to produce null alleles of their target gene by antibody         staining and flow cytometry. The authors showed that         optimization of the PAM improved activity and also provided an         on-line tool for designing sgRNAs.     -   Swiech et al. demonstrate that AAV-mediated SpCas9 genome         editing can enable reverse genetic studies of gene function in         the brain.     -   Konermann et al. (2015) discusses the ability to attach multiple         effector domains, e.g., transcriptional activator, functional         and epigenomic regulators at appropriate positions on the guide         such as stem or tetraloop with and without linkers.     -   Zetsche et al. demonstrates that the Cas9 enzyme can be split         into two and hence the assembly of Cas9 for activation can be         controlled.     -   Chen et al. relates to multiplex screening by demonstrating that         a genome-wide in vivo CRISPR-Cas9 screen in mice reveals genes         regulating lung metastasis.     -   Ran et al. (2015) relates to SaCas9 and its ability to edit         genomes and demonstrates that one cannot extrapolate from         biochemical assays. Shalem et al. (2015) described ways in which         catalytically inactive Cas9 (dCas9) fusions are used to         synthetically repress (CRISPRi) or activate (CRISPRa)         expression, showing, advances using Cas9 for genome-scale         screens, including arrayed and pooled screens, knockout         approaches that inactivate genomic loci and strategies that         modulate transcriptional activity.     -   Shalem et al. (2015) described ways in which catalytically         inactive Cas9 (dCas9) fusions are used to synthetically repress         (CRISPRi) or activate (CRISPRa) expression, showing, advances         using Cas9 for genome-scale screens, including arrayed and         pooled screens, knockout approaches that inactivate genomic loci         and strategies that modulate transcriptional activity.     -   Xu et al. (2015) assessed the DNA sequence features that         contribute to single guide RNA (sgRNA) efficiency in         CRISPR-based screens. The authors explored efficiency of         CRISPR/Cas9 knockout and nucleotide preference at the cleavage         site. The authors also found that the sequence preference for         CRISPRi/a is substantially different from that for CRISPR/Cas9         knockout.     -   Parnas et al. (2015) introduced genome-wide pooled CRISPR-Cas9         libraries into dendritic cells (DCs) to identify genes that         control the induction of tumor necrosis factor (Tnf) by         bacterial lipopolysaccharide (LPS). Known regulators of Tlr4         signaling and previously unknown candidates were identified and         classified into three functional modules with distinct effects         on the canonical responses to LPS.     -   Ramanan et al (2015) demonstrated cleavage of viral episomal DNA         (cccDNA) in infected cells. The HBV genome exists in the nuclei         of infected hepatocytes as a 3.2 kb double-stranded episomal DNA         species called covalently closed circular DNA (cccDNA), which is         a key component in the HBV life cycle whose replication is not         inhibited by current therapies. The authors showed that sgRNAs         specifically targeting highly conserved regions of HBV robustly         suppresses viral replication and depleted cccDNA.     -   Nishimasu et al. (2015) reported the crystal structures of         SaCas9 in complex with a single guide RNA (sgRNA) and its         double-stranded DNA targets, containing the 5′-TTGAAT-3′ PAM and         the 5′-TTGGGT-3′ PAM. A structural comparison of SaCas9 with         SpCas9 highlighted both structural conservation and divergence,         explaining their distinct PAM specificities and orthologous         sgRNA recognition.

Mention is also made of Tsai et al, “Dimeric CRISPR RNA-guided Fokl nucleases for highly specific genome editing,” Nature Biotechnology 32(6): 569-77 (2014) which is not believed to be prior art to the instant invention or application, but which may be considered in the practice of the instant invention. Mention is also made of Konermann et al., “Genome-scale transcription activation by an engineered CRISPR-Cas9 complex,” doi: 10.1038/naturel4136, incorporated herein by reference.

In general, the CRISPR-Cas or CRISPR system is as used in the foregoing documents, such as WO 2014/093622 (PCT/US2013/074667) and refers collectively to transcripts and other elements involved in the expression of or directing the activity of CRISPR-associated (“Cas”) genes, including sequences encoding a Cas gene, a tracr (trans-activating CRISPR) sequence (e.g. tracrRNA or an active partial tracrRNA), a tracr-mate sequence (encompassing a “direct repeat” and a tracrRNA-processed partial direct repeat in the context of an endogenous CRISPR system), a guide sequence (also referred to as a “spacer” in the context of an endogenous CRISPR system), or “RNA(s)” as that term is herein used (e.g., RNA(s) to guide Cas9, e.g. CRISPR RNA and transactivating (tracr) RNA or a single guide RNA (sgRNA) (chimeric RNA)) or other sequences and transcripts from a CRISPR locus. In general, a CRISPR system is characterized by elements that promote the formation of a CRISPR complex at the site of a target sequence (also referred to as a protospacer in the context of an endogenous CRISPR system). In the context of formation of a CRISPR complex, “target sequence” refers to a sequence to which a guide sequence is designed to have complementarity, where hybridization between a target sequence and a guide sequence promotes the formation of a CRISPR complex. A target sequence may comprise any polynucleotide, such as DNA or RNA polynucleotides. In some embodiments, a target sequence is located in the nucleus or cytoplasm of a cell. In some embodiments, direct repeats may be identified in silico by searching for repetitive motifs that fulfill any or all of the following criteria: 1. found in a 2 Kb window of genomic sequence flanking the type II CRISPR locus; 2. span from 20 to 50 bp; and 3. interspaced by 20 to 50 bp. In some embodiments, 2 of these criteria may be used, for instance 1 and 2, 2 and 3, or 1 and 3. In some embodiments, all 3 criteria may be used. In some embodiments it may be preferred in a CRISPR complex that the tracr sequence has one or more hairpins and is 30 or more nucleotides in length, 40 or more nucleotides in length, or 50 or more nucleotides in length; the guide sequence is between 10 to 30 nucleotides in length, the CRISPR/Cas enzyme is a Type II Cas9 enzyme. In embodiments of the invention the terms guide sequence and guide RNA are used interchangeably as in foregoing cited documents such as WO 2014/093622 (PCT/US2013/074667). In general, a guide sequence is any polynucleotide sequence having sufficient complementarity with a target polynucleotide sequence to hybridize with the target sequence and direct sequence-specific binding of a CRISPR complex to the target sequence. In some embodiments, the degree of complementarity between a guide sequence and its corresponding target sequence, when optimally aligned using a suitable alignment algorithm, is about or more than about 50%, 60%, 75%, 80%, 85%, 90%, 95%, 97.5%, 99%, or more. Optimal alignment may be determined with the use of any suitable algorithm for aligning sequences, non-limiting example of which include the Smith-Waterman algorithm, the Needleman-Wunsch algorithm, algorithms based on the Burrows-Wheeler Transform (e.g. the Burrows Wheeler Aligner), ClustalW, Clustal X, BLAT, Novoalign (available online from Novocraft Technologies), ELAND (Illumina, San Diego, Calif.), SOAP (see Li, et al., Bioinformatics, 2008 Mar. 1; 24(5):713-4), and Maq (Mapping and Assembly with Quality project hosted online by SourceForge). In some embodiments, a guide sequence is about or more than about 5, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 75, or more nucleotides in length. In some embodiments, a guide sequence is less than about 75, 50, 45, 40, 35, 30, 25, 20, 15, 12, or fewer nucleotides in length. Preferably the guide sequence is 10-30 nucleotides long. The ability of a guide sequence to direct sequence-specific binding of a CRISPR complex to a target sequence may be assessed by any suitable assay. For example, the components of a CRISPR system sufficient to form a CRISPR complex, including the guide sequence to be tested, may be provided to a host cell having the corresponding target sequence, such as by transfection with vectors encoding the components of the CRISPR sequence, followed by an assessment of preferential cleavage within the target sequence, such as by Surveyor assay as described herein. Similarly, cleavage of a target polynucleotide sequence may be evaluated in a test tube by providing the target sequence, components of a CRISPR complex, including the guide sequence to be tested and a control guide sequence different from the test guide sequence, and comparing binding or rate of cleavage at the target sequence between the test and control guide sequence reactions. Other assays are possible, and will occur to those skilled in the art. A guide sequence may be selected to target any target sequence. In some embodiments, the target sequence is a sequence within a genome of a cell. Exemplary target sequences include those that are unique in the target genome. For example, for the S. pyogenes Cas9, a unique target sequence in a genome may include a Cas9 target site of the form MMMMMMMMNNNNNNNNNNNNXGG (SEQ ID NO: 1) where NNNNNNNNNNNNXGG (SEQ ID NO: 2) (N is A, G, T, or C; and X can be anything) has a single occurrence in the genome. A unique target sequence in a genome may include an S. pyogenes Cas9 target site of the form MMMMMMMMMNNNNNNNNNNNXGG (SEQ ID NO: 3) where NNNNNNNNNNNXGG (SEQ ID NO: 4) (N is A, G, T, or C; and X can be anything) has a single occurrence in the genome. For the S. thermophilus CRISPRl Cas9, a unique target sequence in a genome may include a Cas9 target site of the form MMMMMMMMNNNNNNNNNNNNXXAGAAW (SEQ ID NO: 5) where NNNNNNNNNNNNXXAGAAW (SEQ ID NO: 6) (N is A, G, T, or C; X can be anything; and W is A or T) has a single occurrence in the genome. A unique target sequence in a genome may include an S. thermophilus CRISPRl Cas9 target site of the form MMMMMMMMMNNNNNNNNNNNXXAGAAW (SEQ ID NO: 7) where NNNNNNNNNNNXXAGAAW (SEQ ID NO: 8) (N is A, G, T, or C; X can be anything; and W is A or T) has a single occurrence in the genome. For the S. pyogenes Cas9, a unique target sequence in a genome may include a Cas9 target site of the form MMMMMMMMNNNNNNNNNNXGGXG (SEQ ID NO: 9) where NNNNNNNNNNNNXGGXG (SEQ ID NO: 10) (N is A, G, T, or C; and X can be anything) has a single occurrence in the genome. A unique target sequence in a genome may include an S. pyogenes Cas9 target site of the form MMMMMMMMMNNNNNNNNNNNXGGXG (SEQ ID NO: 11) where NNNNNNNNNNNXGGXG (SEQ ID NO: 12) (N is A, G, T, or C; and X can be anything) has a single occurrence in the genome. In each of these sequences “M” may be A, G, T, or C, and need not be considered in identifying a sequence as unique. In some embodiments, a guide sequence is selected to reduce the degree secondary structure within the guide sequence. In some embodiments, about or less than about 75%, 50%, 40%, 30%, 25%, 20%, 15%, 10%, 5%, 1%, or fewer of the nucleotides of the guide sequence participate in self-complementary base pairing when optimally folded. Optimal folding may be determined by any suitable polynucleotide folding algorithm. Some programs are based on calculating the minimal Gibbs free energy. An example of one such algorithm is mFold, as described by Zuker and Stiegler (Nucleic Acids Res. 9 (1981), 133-148). Another example folding algorithm is the online webserver RNAfold, developed at the Institute for Theoretical Chemistry at the University of Vienna, using the centroid structure prediction algorithm (see e.g. A. R. Gruber et al., 2008, Cell 106(1): 23-24; and P A Carr and G M Church, 2009, Nature Biotechnology 27(12): 1151-62).

In general, a tracr mate sequence includes any sequence that has sufficient complementarity with a tracr sequence to promote one or more of: (1) excision of a guide sequence flanked by tracr mate sequences in a cell containing the corresponding tracr sequence; and (2) formation of a CRISPR complex at a target sequence, wherein the CRISPR complex comprises the tracr mate sequence hybridized to the tracr sequence. In general, degree of complementarity is with reference to the optimal alignment of the tracr mate sequence and tracr sequence, along the length of the shorter of the two sequences. Optimal alignment may be determined by any suitable alignment algorithm, and may further account for secondary structures, such as self-complementarity within either the tracr sequence or tracr mate sequence. In some embodiments, the degree of complementarity between the tracr sequence and tracr mate sequence along the length of the shorter of the two when optimally aligned is about or more than about 25%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 97.5%, 99%, or higher. In some embodiments, the tracr sequence is about or more than about 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 40, 50, or more nucleotides in length. In some embodiments, the tracr sequence and tracr mate sequence are contained within a single transcript, such that hybridization between the two produces a transcript having a secondary structure, such as a hairpin. In an embodiment of the invention, the transcript or transcribed polynucleotide sequence has at least two or more hairpins. In preferred embodiments, the transcript has two, three, four or five hairpins. In a further embodiment of the invention, the transcript has at most five hairpins. In a hairpin structure the portion of the sequence 5′ of the final “N” and upstream of the loop corresponds to the tracr mate sequence, and the portion of the sequence 3′ of the loop corresponds to the tracr sequence Further non-limiting examples of single polynucleotides comprising a guide sequence, a tracr mate sequence, and a tracr sequence are as follows (listed 5′ to 3′), where “N” represents a base of a guide sequence, the first block of lower case letters represent the tracr mate sequence, and the second block of lower case letters represent the tracr sequence, and the final poly-T sequence represents the transcription terminator: (1) NNNNNNNNNNNNNNNNNNNNgtttttgtactctcaagatttaGAAAtaaatcttgcagaagctacaaagataa ggcttcatgccgaaatcaacaccctgtcattttatggcagggtgttttcgttatttaaTTTTTT (SEQ ID NO: 13); (2) NNNNNNNNNNNNNNNNNNNNgtttttgtactctcaGAAAtgcagaagctacaaagataaggcttcatgccg aaatcaacaccctgtcattttatggcagggtgttttcgttatttaaTTTTTT (SEQ ID NO: 14); (3) NNNNNNNNNNNNNNNNNNNNgtttttgtactctcaGAAAtgcagaagctacaaagataaggcttcatgccg aaatcaacaccctgtcattttatggcagggtgtTTTTTT (SEQ ID NO: 15); (4) NNNNNNNNNNNNNNNNNNNNgttttagagctaGAAAtagcaagttaaaataaggctagtccgttatcaactt gaaaaagtggcaccgagtcggtgcTTTTTT (SEQ ID NO: 16); (5) NNNNNNNNNNNNNNNNNNNNgttttagagctaGAAATAGcaagttaaaataaggctagtccgttatcaac ttgaaaaagtgTTTTTTT (SEQ ID NO: 17); and (6) NNNNNNNNNNNNNNNNNNNNgttttagagctagAAATAGcaagttaaaataaggctagtccgttatcaTT TTTTTT (SEQ ID NO: 18). In some embodiments, sequences (1) to (3) are used in combination with Cas9 from S. thermophilus CRISPRl. In some embodiments, sequences (4) to (6) are used in combination with Cas9 from S. pyogenes. In some embodiments, the tracr sequence is a separate transcript from a transcript comprising the tracr mate sequence.

In some embodiments, chimeric synthetic guide RNAs (sgRNAs) designs may incorporate at least 12 bp of duplex structure between the direct repeat and tracrRNA

For minimization of toxicity and off-target effect, it will be important to control the concentration of CRISPR enzyme mRNA and guide RNA delivered. Optimal concentrations of CRISPR enzyme mRNA and guide RNA can be determined by testing different concentrations in a cellular or non-human eukaryote animal model and using deep sequencing the analyze the extent of modification at potential off-target genomic loci. For example, for the guide sequence targeting 5′-GAGTCCGAGCAGAAGAAGAA-3′ (SEQ ID NO: 19) in the EMX1 gene of the human genome, deep sequencing can be used to assess the level of modification at the following two off-target loci, 1: 5′-GAGTCCTAGCAGGAGAAGAA-3′ (SEQ ID NO: 20) and 2: 5′-GAGTCTAAGCAGAAGAAGAA-3′ (SEQ ID NO: 21). The concentration that gives the highest level of on-target modification while minimizing the level of off-target modification should be chosen for in vivo delivery. Alternatively, to minimize the level of toxicity and off-target effect, CRISPR enzyme nickase mRNA (for example S. pyogenes Cas9 with the D10A mutation) can be delivered with a pair of guide RNAs targeting a site of interest. The two guide RNAs need to be spaced as follows. Guide sequences and strategies to mimize toxicity and off-target effects can be as in WO 2014/093622 (PCT/US2013/074667).

The CRISPR system is derived advantageously from a type II CRISPR system. In some embodiments, one or more elements of a CRISPR system is derived from a particular organism comprising an endogenous CRISPR system, such as Streptococcus pyogenes. In preferred embodiments of the invention, the CRISPR system is a type II CRISPR system and the Cas enzyme is Cas9, which catalyzes DNA cleavage. Non-limiting examples of Cas proteins include Cas1, Cas1B, Cas2, Cas3, Cas4, Cas5, Cas6, Cas7, Cas8, Cas9 (also known as Csn1 and Csx12), Cas10, Csy1, Csy2, Csy3, Cse1, Cse2, Csc1, Csc2, Csa5, Csn2, Csm2, Csm3, Csm4, Csm5, Csm6, Cmr1, Cmr3, Cmr4, Cmr5, Cmr6, Csb1, Csb2, Csb3, Csx17, Csx14, Csx10, Csx16, CsaX, Csx3, Csx1, Csx15, Csf1, Csf2, Cs3, Csf4, homologues thereof, or modified versions thereof.

In some embodiments, the unmodified CRISPR enzyme has DNA cleavage activity, such as Cas9. In some embodiments, the CRISPR enzyme directs cleavage of one or both strands at the location of a target sequence, such as within the target sequence and/or within the complement of the target sequence. In some embodiments, the CRISPR enzyme directs cleavage of one or both strands within about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 50, 100, 200, 500, or more base pairs from the first or last nucleotide of a target sequence. In some embodiments, a vector encodes a CRISPR enzyme that is mutated to with respect to a corresponding wild-type enzyme such that the mutated CRISPR enzyme lacks the ability to cleave one or both strands of a target polynucleotide containing a target sequence. For example, an aspartate-to-alanine substitution (DIOA) in the RuvC I catalytic domain of Cas9 from S. pyogenes converts Cas9 from a nuclease that cleaves both strands to a nickase (cleaves a single strand). Other examples of mutations that render Cas9 a nickase include, without limitation, H840A, N854A, and N863A. As a further example, two or more catalytic domains of Cas9 (RuvC I, RuvC II, and RuvC III or the HNH domain) may be mutated to produce a mutated Cas9 substantially lacking all DNA cleavage activity. In some embodiments, a DIOA mutation is combined with one or more of H840A, N854A, or N863A mutations to produce a Cas9 enzyme substantially lacking all DNA cleavage activity. In some embodiments, a CRISPR enzyme is considered to substantially lack all DNA cleavage activity when the DNA cleavage activity of the mutated enzyme is about no more than 25%, 10%, 5%, 1%, 0.1%, 0.01%, or less of the DNA cleavage activity of the non-mutated form of the enzyme; an example can be when the DNA cleavage activity of the mutated form is nil or negligible as compared with the non-mutated form. Where the enzyme is not SpCas9, mutations may be made at any or all residues corresponding to positions 10, 762, 840, 854, 863 and/or 986 of SpCas9 (which may be ascertained for instance by standard sequence comparison tools). In particular, any or all of the following mutations are preferred in SpCas9: DIOA, E762A, H840A, N854A, N863A and/or D986A; as well as conservative substitution for any of the replacement amino acids is also envisaged. The same (or conservative substitutions of these mutations) at corresponding positions in other Cas9s are also preferred. Particularly preferred are D10 and H840 in SpCas9. However, in other Cas9s, residues corresponding to SpCas9 D10 and H840 are also preferred. Orthologs of SpCas9 can be used in the practice of the invention. A Cas enzyme may be identified Cas9 as this can refer to the general class of enzymes that share homology to the biggest nuclease with multiple nuclease domains from the type II CRISPR system. Most preferably, the Cas9 enzyme is from, or is derived from, spCas9 (S. pyogenes Cas9) or saCas9 (S. aureus Cas9). StCas9” refers to wild type Cas9 from S. thermophilus, the protein sequence of which is given in the SwissProt database under accession number G3ECR1. Similarly, S pyogenes Cas9 or spCas9 is included in SwissProt under accession number Q99ZW2. By derived, Applicants mean that the derived enzyme is largely based, in the sense of having a high degree of sequence homology with, a wildtype enzyme, but that it has been mutated (modified) in some way as described herein. It will be appreciated that the terms Cas and CRISPR enzyme are generally used herein interchangeably, unless otherwise apparent. As mentioned above, many of the residue numberings used herein refer to the Cas9 enzyme from the type II CRISPR locus in Streptococcus pyogenes. However, it will be appreciated that this invention includes many more Cas9s from other species of microbes, such as SpCas9, SaCa9, StlCas9 and so forth. Enzymatic action by Cas9 derived from Streptococcus pyogenes or any closely related Cas9 generates double stranded breaks at target site sequences which hybridize to 20 nucleotides of the guide sequence and that have a protospacer-adjacent motif (PAM) sequence (examples include NGG/NRG or a PAM that can be determined as described herein) following the 20 nucleotides of the target sequence. CRISPR activity through Cas9 for site-specific DNA recognition and cleavage is defined by the guide sequence, the tracr sequence that hybridizes in part to the guide sequence and the PAM sequence. More aspects of the CRISPR system are described in Karginov and Hannon, The CRISPR system: small RNA-guided defence in bacteria and archaea, Mole Cell 2010, Jan. 15; 37(1): 7. The type II CRISPR locus from Streptococcus pyogenes SF370, which contains a cluster of four genes Cas9, Cas1, Cas2, and Csn1, as well as two non-coding RNA elements, tracrRNA and a characteristic array of repetitive sequences (direct repeats) interspaced by short stretches of non-repetitive sequences (spacers, about 30 bp each). In this system, targeted DNA double-strand break (DSB) is generated in four sequential steps. First, two non-coding RNAs, the pre-crRNA array and tracrRNA, are transcribed from the CRISPR locus. Second, tracrRNA hybridizes to the direct repeats of pre-crRNA, which is then processed into mature crRNAs containing individual spacer sequences. Third, the mature crRNA:tracrRNA complex directs Cas9 to the DNA target consisting of the protospacer and the corresponding PAM via heteroduplex formation between the spacer region of the crRNA and the protospacer DNA. Finally, Cas9 mediates cleavage of target DNA upstream of PAM to create a DSB within the protospacer. A pre-crRNA array consisting of a single spacer flanked by two direct repeats (DRs) is also encompassed by the term “tracr-mate sequences”). In certain embodiments, Cas9 may be constitutively present or inducibly present or conditionally present or administered or delivered. Cas9 optimization may be used to enhance function or to develop new functions, one can generate chimeric Cas9 proteins. And Cas9 may be used as a generic DNA binding protein.

Typically, in the context of an endogenous CRISPR system, formation of a CRISPR complex (comprising a guide sequence hybridized to a target sequence and complexed with one or more Cas proteins) results in cleavage of one or both strands in or near (e.g. within 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, or more base pairs from) the target sequence. Without wishing to be bound by theory, the tracr sequence, which may comprise or consist of all or a portion of a wild-type tracr sequence (e.g. about or more than about 20, 26, 32, 45, 48, 54, 63, 67, 85, or more nucleotides of a wild-type tracr sequence), may also form part of a CRISPR complex, such as by hybridization along at least a portion of the tracr sequence to all or a portion of a tracr mate sequence that is operably linked to the guide sequence.

An example of a codon optimized sequence, is in this instance a sequence optimized for expression in a eukaryote, e.g., humans (i.e. being optimized for expression in humans), or for another eukaryote, animal or mammal as herein discussed; see, e.g., SaCas9 human codon optimized sequence in WO 2014/093622 (PCT/US2013/074667). Whilst this is preferred, it will be appreciated that other examples are possible and codon optimization for a host species other than human, or for codon optimization for specific organs is known. In some embodiments, an enzyme coding sequence encoding a CRISPR enzyme is codon optimized for expression in particular cells, such as eukaryotic cells. The eukaryotic cells may be those of or derived from a particular organism, such as a mammal, including but not limited to human, or non-human eukaryote or animal or mammal as herein discussed, e.g., mouse, rat, rabbit, dog, livestock, or non-human mammal or primate. In some embodiments, processes for modifying the germ line genetic identity of human beings and/or processes for modifying the genetic identity of animals which are likely to cause them suffering without any substantial medical benefit to man or animal, and also animals resulting from such processes, may be excluded. In general, codon optimization refers to a process of modifying a nucleic acid sequence for enhanced expression in the host cells of interest by replacing at least one codon (e.g. about or more than about 1, 2, 3, 4, 5, 10, 15, 20, 25, 50, or more codons) of the native sequence with codons that are more frequently or most frequently used in the genes of that host cell while maintaining the native amino acid sequence. Various species exhibit particular bias for certain codons of a particular amino acid. Codon bias (differences in codon usage between organisms) often correlates with the efficiency of translation of messenger RNA (mRNA), which is in turn believed to be dependent on, among other things, the properties of the codons being translated and the availability of particular transfer RNA (tRNA) molecules. The predominance of selected tRNAs in a cell is generally a reflection of the codons used most frequently in peptide synthesis. Accordingly, genes can be tailored for optimal gene expression in a given organism based on codon optimization. Codon usage tables are readily available, for example, at the “Codon Usage Database” available through the server at Kazusa DNA Research Institute, and these tables can be adapted in a number of ways. See Nakamura, Y., et al. “Codon usage tabulated from the international DNA sequence databases: status for the year 2000” Nucl. Acids Res. 28:292 (2000). Computer algorithms for codon optimizing a particular sequence for expression in a particular host cell are also available, such as Gene Forge (Aptagen; Jacobus, PA), are also available. In some embodiments, one or more codons (e.g. 1, 2, 3, 4, 5, 10, 15, 20, 25, 50, or more, or all codons) in a sequence encoding a CRISPR enzyme correspond to the most frequently used codon for a particular amino acid.

In some embodiments, a vector encodes a CRISPR enzyme comprising one or more nuclear localization sequences (NLSs), such as about or more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more NLSs. In some embodiments, the CRISPR enzyme comprises about or more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more NLSs at or near the amino-terminus, about or more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more NLSs at or near the carboxy-terminus, or a combination of these (e.g. zero or at least one or more NLS at the amino-terminus and zero or at one or more NLS at the carboxy terminus). When more than one NLS is present, each may be selected independently of the others, such that a single NLS may be present in more than one copy and/or in combination with one or more other NLSs present in one or more copies. In a preferred embodiment of the invention, the CRISPR enzyme comprises at most 6 NLSs. In some embodiments, an NLS is considered near the N- or C-terminus when the nearest amino acid of the NLS is within about 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 40, 50, or more amino acids along the polypeptide chain from the N- or C-terminus. Non-limiting examples of NLSs include an NLS sequence derived from: the NLS of the SV40 virus large T-antigen, having the amino acid sequence PKKKRKV (SEQ ID NO: 22); the NLS from nucleoplasmin (e.g. the nucleoplasmin bipartite NLS with the sequence KRPAATKKAGQAKKKK (SEQ ID NO: 23)); the c-myc NLS having the amino acid sequence PAAKRVKLD (SEQ ID NO: 24) or RQRRNELKRSP (SEQ ID NO: 25); the hRNPAl M9 NLS having the sequence NQSSNFGPMKGGNFGGRSSGPYGGGGQYFAKPRNQGGY (SEQ ID NO: 26); the sequence RMRIZFKNKGKDTAELRRRRVEVSVELRKAKKDEQILKRRNV (SEQ ID NO: 27) of the IBB domain from importin-alpha; the sequences VSRKRPRP (SEQ ID NO: 28) and PPKKARED (SEQ ID NO: 29) of the myoma T protein; the sequence PQPKKKPL (SEQ ID NO: 30) of human p53; the sequence SALIKKKKKMAP (SEQ ID NO: 31) of mouse c-abl IV; the sequences DRLRR (SEQ ID NO: 32) and PKQKKRK (SEQ ID NO: 33) of the influenza virus NSl; the sequence RKLKKKIKKL (SEQ ID NO: 34) of the Hepatitis virus delta antigen; the sequence REKKKFLKRR (SEQ ID NO: 35) of the mouse Mxl protein; the sequence KRKGDEVDGVDEVAKKKSKK (SEQ ID NO: 36) of the human poly(ADP-ribose) polymerase; and the sequence RKCLQAGMNLEARKTKK (SEQ ID NO: 37) of the steroid hormone receptors (human) glucocorticoid. In general, the one or more NLSs are of sufficient strength to drive accumulation of the CRISPR enzyme in a detectable amount in the nucleus of a eukaryotic cell. In general, strength of nuclear localization activity may derive from the number of NLSs in the CRISPR enzyme, the particular NLS(s) used, or a combination of these factors. Detection of accumulation in the nucleus may be performed by any suitable technique. For example, a detectable marker may be fused to the CRISPR enzyme, such that location within a cell may be visualized, such as in combination with a means for detecting the location of the nucleus (e.g. a stain specific for the nucleus such as DAPI). Cell nuclei may also be isolated from cells, the contents of which may then be analyzed by any suitable process for detecting protein, such as immunohistochemistry, Western blot, or enzyme activity assay. Accumulation in the nucleus may also be determined indirectly, such as by an assay for the effect of CRISPR complex formation (e.g. assay for DNA cleavage or mutation at the target sequence, or assay for altered gene expression activity affected by CRISPR complex formation and/or CRISPR enzyme activity), as compared to a control no exposed to the CRISPR enzyme or complex, or exposed to a CRISPR enzyme lacking the one or more NLSs.

Aspects of the invention relate to the expression of the gene product being decreased or a template polynucleotide being further introduced into the DNA molecule encoding the gene product or an intervening sequence being excised precisely by allowing the two 5′ overhangs to reanneal and ligate or the activity or function of the gene product being altered or the expression of the gene product being increased. In an embodiment of the invention, the gene product is a protein. Only sgRNA pairs creating 5′ overhangs with less than 8 bp overlap between the guide sequences (offset greater than −8 bp) were able to mediate detectable indel formation. Importantly, each guide used in these assays is able to efficiently induce indels when paired with wildtype Cas9, indicating that the relative positions of the guide pairs are the most important parameters in predicting double nicking activity. Since Cas9n and Cas9H840A nick opposite strands of DNA, substitution of Cas9n with Cas9H840A with a given sgRNA pair should have resulted in the inversion of the overhang type; but no indel formation is observed as with Cas9H840A indicating that Cas9H840A is a CRISPR enzyme substantially lacking all DNA cleavage activity (which is when the DNA cleavage activity of the mutated enzyme is about no more than 25%, 10%, 5%, 1%, 0.1%, 0.01%, or less of the DNA cleavage activity of the non-mutated form of the enzyme; whereby an example can be when the DNA cleavage activity of the mutated form is nil or negligible as compared with the non-mutated form, e.g., when no indel formation is observed as with Cas9H840A in the eukaryotic system in contrast to the biochemical or prokaryotic systems). Nonetheless, a pair of sgRNAs that will generate a 5′ overhang with Cas9n should in principle generate the corresponding 3′ overhang instead, and double nicking. Therefore, sgRNA pairs that lead to the generation of a 3′ overhang with Cas9n can be used with another mutated Cas9 to generate a 5′ overhang, and double nicking. Accordingly, in some embodiments, a recombination template is also provided. A recombination template may be a component of another vector as described herein, contained in a separate vector, or provided as a separate polynucleotide. In some embodiments, a recombination template is designed to serve as a template in homologous recombination, such as within or near a target sequence nicked or cleaved by a CRISPR enzyme as a part of a CRISPR complex. A template polynucleotide may be of any suitable length, such as about or more than about 10, 15, 20, 25, 50, 75, 100, 150, 200, 500, 1000, or more nucleotides in length. In some embodiments, the template polynucleotide is complementary to a portion of a polynucleotide comprising the target sequence. When optimally aligned, a template polynucleotide might overlap with one or more nucleotides of a target sequences (e.g. about or more than about 1, 5, 10, 15, 20, or more nucleotides). In some embodiments, when a template sequence and a polynucleotide comprising a target sequence are optimally aligned, the nearest nucleotide of the template polynucleotide is within about 1, 5, 10, 15, 20, 25, 50, 75, 100, 200, 300, 400, 500, 1000, 5000, 10000, or more nucleotides from the target sequence.

In some embodiments, one or more vectors driving expression of one or more elements of a CRISPR system are introduced into a host cell such that expression of the elements of the CRISPR system direct formation of a CRISPR complex at one or more target sites. For example, a Cas enzyme, a guide sequence linked to a tracr-mate sequence, and a tracr sequence could each be operably linked to separate regulatory elements on separate vectors. Or, RNA(s) of the CRISPR System can be delivered to a transgenic Cas9 animal or mammal, e.g., an animal or mammal that constitutively or inducibly or conditionally expresses Cas9; or an animal or mammal that is otherwise expressing Cas9 or has cells containing Cas9, such as by way of prior administration thereto of a vector or vectors that code for and express in vivo Cas9. Alternatively, two or more of the elements expressed from the same or different regulatory elements, may be combined in a single vector, with one or more additional vectors providing any components of the CRISPR system not included in the first vector. CRISPR system elements that are combined in a single vector may be arranged in any suitable orientation, such as one element located 5′ with respect to (“upstream” of) or 3′ with respect to (“downstream” of) a second element. The coding sequence of one element may be located on the same or opposite strand of the coding sequence of a second element, and oriented in the same or opposite direction. In some embodiments, a single promoter drives expression of a transcript encoding a CRISPR enzyme and one or more of the guide sequence, tracr mate sequence (optionally operably linked to the guide sequence), and a tracr sequence embedded within one or more intron sequences (e.g. each in a different intron, two or more in at least one intron, or all in a single intron). In some embodiments, the CRISPR enzyme, guide sequence, tracr mate sequence, and tracr sequence are operably linked to and expressed from the same promoter. Delivery vehicles, vectors, particles, nanoparticles, formulations and components thereof for expression of one or more elements of a CRISPR system are as used in the foregoing documents, such as WO 2014/093622 (PCT/US2013/074667). In some embodiments, a vector comprises one or more insertion sites, such as a restriction endonuclease recognition sequence (also referred to as a “cloning site”). In some embodiments, one or more insertion sites (e.g. about or more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more insertion sites) are located upstream and/or downstream of one or more sequence elements of one or more vectors. In some embodiments, a vector comprises an insertion site upstream of a tracr mate sequence, and optionally downstream of a regulatory element operably linked to the tracr mate sequence, such that following insertion of a guide sequence into the insertion site and upon expression the guide sequence directs sequence-specific binding of a CRISPR complex to a target sequence in a eukaryotic cell. In some embodiments, a vector comprises two or more insertion sites, each insertion site being located between two tracr mate sequences so as to allow insertion of a guide sequence at each site. In such an arrangement, the two or more guide sequences may comprise two or more copies of a single guide sequence, two or more different guide sequences, or combinations of these. When multiple different guide sequences are used, a single expression construct may be used to target CRISPR activity to multiple different, corresponding target sequences within a cell. For example, a single vector may comprise about or more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, or more guide sequences. In some embodiments, about or more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more such guide-sequence-containing vectors may be provided, and optionally delivered to a cell. In some embodiments, a vector comprises a regulatory element operably linked to an enzyme-coding sequence encoding a CRISPR enzyme, such as a Cas protein. CRISPR enzyme or CRISPR enzyme mRNA or CRISPR guide RNA or RNA(s) can be delivered separately; and advantageously at least one of these is delivered via a nanoparticle complex. CRISPR enzyme mRNA can be delivered prior to the guide RNA to give time for CRISPR enzyme to be expressed. CRISPR enzyme mRNA might be administered 1-12 hours (preferably around 2-6 hours) prior to the administration of guide RNA. Alternatively, CRISPR enzyme mRNA and guide RNA can be administered together. Advantageously, a second booster dose of guide RNA can be administered 1-12 hours (preferably around 2-6 hours) after the initial administration of CRISPR enzyme mRNA+guide RNA. Additional administrations of CRISPR enzyme mRNA and/or guide RNA might be useful to achieve the most efficient levels of genome modification.

In one aspect, the invention provides methods for using one or more elements of a CRISPR system. The CRISPR complex of the invention provides an effective means for modifying a target polynucleotide. The CRISPR complex of the invention has a wide variety of utility including modifying (e.g., deleting, inserting, translocating, inactivating, activating) a target polynucleotide in a multiplicity of cell types. As such the CRISPR complex of the invention has a broad spectrum of applications in, e.g., gene therapy, drug screening, disease diagnosis, and prognosis. An exemplary CRISPR complex comprises a CRISPR enzyme complexed with a guide sequence hybridized to a target sequence within the target polynucleotide. The guide sequence is linked to a tracr mate sequence, which in turn hybridizes to a tracr sequence. In one embodiment, this invention provides a method of cleaving a target polynucleotide. The method comprises modifying a target polynucleotide using a CRISPR complex that binds to the target polynucleotide and effect cleavage of said target polynucleotide. Typically, the CRISPR complex of the invention, when introduced into a cell, creates a break (e.g., a single or a double strand break) in the genome sequence. For example, the method can be used to cleave a disease gene in a cell. The break created by the CRISPR complex can be repaired by a repair processes such as the error prone non-homologous end joining (NHEJ) pathway or the high fidelity homo logy-directed repair (HDR). During these repair process, an exogenous polynucleotide template can be introduced into the genome sequence. In some methods, the HDR process is used modify genome sequence. For example, an exogenous polynucleotide template comprising a sequence to be integrated flanked by an upstream sequence and a downstream sequence is introduced into a cell. The upstream and downstream sequences share sequence similarity with either side of the site of integration in the chromosome. Where desired, a donor polynucleotide can be DNA, e.g., a DNA plasmid, a bacterial artificial chromosome (BAC), a yeast artificial chromosome (YAC), a viral vector, a linear piece of DNA, a PCR fragment, a naked nucleic acid, or a nucleic acid complexed with a delivery vehicle such as a liposome or poloxamer. The exogenous polynucleotide template comprises a sequence to be integrated (e.g., a mutated gene). The sequence for integration may be a sequence endogenous or exogenous to the cell. Examples of a sequence to be integrated include polynucleotides encoding a protein or a non-coding RNA (e.g., a microRNA). Thus, the sequence for integration may be operably linked to an appropriate control sequence or sequences. Alternatively, the sequence to be integrated may provide a regulatory function. The upstream and downstream sequences in the exogenous polynucleotide template are selected to promote recombination between the chromosomal sequence of interest and the donor polynucleotide. The upstream sequence is a nucleic acid sequence that shares sequence similarity with the genome sequence upstream of the targeted site for integration. Similarly, the downstream sequence is a nucleic acid sequence that shares sequence similarity with the chromosomal sequence downstream of the targeted site of integration. The upstream and downstream sequences in the exogenous polynucleotide template can have 75%, 80%>, 85%, 90%>, 95%, or 100% sequence identity with the targeted genome sequence. Preferably, the upstream and downstream sequences in the exogenous polynucleotide template have about 95%, 96%, 97%, 98%, 99%, or 100% sequence identity with the targeted genome sequence. In some methods, the upstream and downstream sequences in the exogenous polynucleotide template have about 99% or 100% sequence identity with the targeted genome sequence. An upstream or downstream sequence may comprise from about 20 bp to about 2500 bp, for example, about 50, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300, 2400, or 2500 bp. In some methods, the exemplary upstream or downstream sequence have

about 200 bp to about 2000 bp, about 600 bp to about 1000 bp, or more particularly about 700 bp to about 1000 bp. In some methods, the exogenous polynucleotide template may further comprise a marker. Such a marker may make it easy to screen for targeted integrations. Examples of suitable markers include restriction sites, fluorescent proteins, or selectable markers. The exogenous polynucleotide template of the invention can be constructed using recombinant techniques (see, for example, Sambrook et al., 2001 and Ausubel et al., 1996). In a method for modifying a target polynucleotide by integrating an exogenous polynucleotide template, a double stranded break is introduced into the genome sequence by the CRISPR complex, the break is repaired via homologous recombination an exogenous polynucleotide template such that the template is integrated into the genome. The presence of a double-stranded break facilitates integration of the template. In other embodiments, this invention provides a method of modifying expression of a polynucleotide in a eukaryotic cell. The method comprises increasing or decreasing expression of a target polynucleotide by using a CRISPR complex that binds to the polynucleotide. In some methods, a target polynucleotide can be inactivated to effect the modification of the expression in a cell. For example, upon the binding of a CRISPR complex to a target sequence in a cell, the target polynucleotide is inactivated such that the sequence is not transcribed, the coded protein is not produced, or the sequence does not function as the wild-type sequence does. For example, a protein or microRNA coding sequence may be inactivated such that the protein or microRNA or pre-microRNA transcript is not produced. In some methods, a control sequence can be inactivated such that it no longer functions as a control sequence. As used herein, “control sequence” refers to any nucleic acid sequence that effects the transcription, translation, or accessibility of a nucleic acid sequence. Examples of a control sequence include, a promoter, a transcription terminator, and an enhancer are control sequences. The target polynucleotide of a CRISPR complex can be any polynucleotide endogenous or exogenous to the eukaryotic cell. For example, the target polynucleotide can be a polynucleotide residing in the nucleus of the eukaryotic cell. The target polynucleotide can be a sequence coding a gene product (e.g., a protein) or a non-coding sequence (e.g., a regulatory polynucleotide or a junk DNA). Examples of target polynucleotides include a sequence associated with a signaling biochemical pathway, e.g., a signaling biochemical pathway-associated gene or polynucleotide. Examples of target polynucleotides include a disease associated gene or polynucleotide. A “disease-associated” gene or polynucleotide refers to any gene or polynucleotide which is yielding transcription or translation products at an abnormal level or in an abnormal form in cells derived from a disease-affected tissues compared with tissues or cells of a non disease control. It may be a gene that becomes expressed at an abnormally high level; it may be a gene that becomes expressed at an abnormally low level, where the altered expression correlates with the occurrence and/or progression of the disease. A disease-associated gene also refers to a gene possessing mutation(s) or genetic variation that is directly responsible or is in linkage disequilibrium with a gene(s) that is responsible for the etiology of a disease. The transcribed or translated products may be known or unknown, and may be at a normal or abnormal level. The target polynucleotide of a CRISPR complex can be any polynucleotide endogenous or exogenous to the eukaryotic cell. For example, the target polynucleotide can be a polynucleotide residing in the nucleus of the eukaryotic cell. The target polynucleotide can be a sequence coding a gene product (e.g., a protein) or a non-coding sequence (e.g., a regulatory polynucleotide or a junk DNA). Without wishing to be bound by theory, it is believed that the target sequence should be associated with a PAM (protospacer adjacent motif); that is, a short sequence recognized by the CRISPR complex. The precise sequence and length requirements for the PAM differ depending on the CRISPR enzyme used, but PAMs are typically 2-5 base pair sequences adjacent the protospacer (that is, the target sequence) Examples of PAM sequences are given in the examples section below, and the skilled person will be able to identify further PAM sequences for use with a given CRISPR enzyme. In some embodiments, the method comprises allowing a CRISPR complex to bind to the target polynucleotide to effect cleavage of said target polynucleotide thereby modifying the target polynucleotide, wherein the CRISPR complex comprises a CRISPR enzyme complexed with a guide sequence hybridized to a target sequence within said target polynucleotide, wherein said guide sequence is linked to a tracr mate sequence which in turn hybridizes to a tracr sequence. In one aspect, the invention provides a method of modifying expression of a polynucleotide in a eukaryotic cell. In some embodiments, the method comprises allowing a CRISPR complex to bind to the polynucleotide such that said binding results in increased or decreased expression of said polynucleotide; wherein the CRISPR complex comprises a CRISPR enzyme complexed with a guide sequence hybridized to a target sequence within said polynucleotide, wherein said guide sequence is linked to a tracr mate sequence which in turn hybridizes to a tracr sequence. Similar considerations and conditions apply as above for methods of modifying a target polynucleotide. In fact, these sampling, culturing and re-introduction options apply across the aspects of the present invention. In one aspect, the invention provides for methods of modifying a target polynucleotide in a eukaryotic cell, which may be in vivo, ex vivo or in vitro. In some embodiments, the method comprises sampling a cell or population of cells from a human or non-human animal, and modifying the cell or cells. Culturing may occur at any stage ex vivo. The cell or cells may even be re-introduced into the non-human animal or plant. For re-introduced cells it is particularly preferred that the cells are stem cells.

Indeed, in any aspect of the invention, the CRISPR complex may comprise a CRISPR enzyme complexed with a guide sequence hybridized to a target sequence, wherein said guide sequence may be linked to a tracr mate sequence which in turn may hybridize to a tracr sequence.

As used herein “diagnosis” or “identifying a patient having” refers to a process of determining if an individual is afflicted with, or has a genetic predisposition to develop, a cardiometabolic disease.

As used herein, a “companion diagnostic” refers to a diagnostic method and or reagent that is used to identify subjects susceptible to treatment with a particular treatment or to monitor treatment and/or to identify an effective dosage for a subject or sub-group or other group of subjects. For purposes herein, a companion diagnostic refers to reagents, such as DNA isolation and sequencing reagents, that are used to detect somatic mutations in a sample. The companion diagnostic refers to the reagents and also to the test(s) that is/are performed with the reagent.

The present invention may be applied to other diseases in addition to cardiometabolic diseases and diseases affiliated with cardiometabolic diseases. In the present application, atherosclerosis is considered a cardiometabolic disease. The methods of the present invention may also be utilized for the diagnosis, prediction and treatment of other cancers in addition to hematological cancer. Other diseases which may be diagnosed, predicted or treated by methods of the present invention include, but are not limited to, autoimmune diseases (such as arthritis), other diseases involving decreased immunity (such as severe infection), dementia (such as Alzheimer's disease), diabetes, hypertension, Progeroid syndromes and other diseases involving in premature aging (such as Alzheimer's disease and Parkinson's disease).

The terms “treat,” treating,” “treatment,” and the like refer to reducing or ameliorating a cardiovascular disease or symptoms associated therewith. It will be appreciated that, although not precluded, treating a cardiovascular disease or the risk of developing a cardiometabolic disease does not require that the disease or the risk be completely eliminated.

In the context of the present invention, a “treatment” is a procedure which alleviates or reduces the negative consequences of a cardiometabolic disease. Many cardiometabolic disease treatments are known in the art, and some are set forth herein. Any treatments or potential treatments can be used in the context of the present invention.

A treatment is not necessarily curative, and may reduce the effect of a cardiovascular disease by a certain percentage over an untreated a cardiovascular disease. The percentage reduction or diminution can be from 10% up to 20, 30, 40, 50, 60, 70, 80, 90, 95, 99 or 100%.

Methods of treatment may be personalized medicine procedures, in which the DNA of an individual is analyzed to provide guidance on the appropriate therapy for that specific individual. The methods of the invention may provide guidance as to whether treatment is necessary, as well as revealing progress of the treatment and guiding the requirement for further treatment of the individual.

As used herein, “inhibiting the development of,” “reducing the risk of,” “prevent,” “preventing,” and the like refer to reducing the probability of developing a cardiometabolic disease in a patient who may not have a cardiometabolic disease, but may have a genetic predisposition to developing a cardiometabolic disease. As used herein, “at risk,” “susceptible to,” or “having a genetic predisposition to,” refers to having a propensity to develop a cardiometabolic disease. For example, a patient having a genetic mutation in a gene associated with a cardiometabolic disease has increased risk (e.g., “higher predisposition”) of developing the disease relative to a control subject having a “lower predisposition” (e.g., a patient without a genetic mutation in a gene associated with a cardiometabolic disease).

As used herein, “reduces,” “reducing,” “inhibit,” or “inhibiting,” may mean a negative alteration of at least 10%, 15%, 25%, 50%, 75%, or 100%.

As used herein, “increases” or “increasing” may mean a positive alteration of at least 10%, 15%, 25%, 50%, 75%, or 100%.

A “therapeutically effective amount” refers to the amount of a compound required to improve, inhibit, or ameliorate a condition of a patient, or a symptom of a disease, in a clinically relevant manner. Any improvement in the patient is considered sufficient to achieve treatment. A sufficient amount of an active compound used to practice the present invention for the treatment of cardiovascular disease varies depending upon the manner of administration, the age, body weight, genotype, and general health of the patient. Ultimately, the prescribers or researchers will decide the appropriate amount and dosage regimen. Such determinations are routine to one of ordinary skill in the art.

From a therapeutic perspective, antihypertensives (such as diuretic medicines, beta-blocking agents, calcium-channel blockers, renin-angiotensin system agents), lipid-modifying medicines, anti-inflammatory agents, nitrates and antiarrhythmic medicines are considered strong candidates for a cardiometabolic disease treatment. Aspects of the invention relate to the administration of antihypertensives (such as diuretic medicines, beta-blocking agents, calcium-channel blockers, renin-angiotensin system agents), lipid-modifying medicines, nitrates and antiarrhythmic medicines separately to individuals in need thereof that may also possess different gene variants associated with a favorable response to each type of administration.

In other embodiments, treatment and/or prevention of cardiometabolic disease may involve aspirin, statins, steroidal or non-steroidal anti-inflammatory drugs, and/or epigenetic modifiers. The epigenetic modifiers may be non-specific DNA synthesis inhibitors, such as DNA methyltransferase inhibitors (such as, but not limited to 5-aza-2′-deoxycytidine or 5-azacytidine) or histone deacetylase inhibitors (such as varinostat, romidepsin, panobinostat, belinostat and entinostat).

Proprotein convertase subtilisin kexin 9 (PCSK9) is a member of the subtilisin serine protease family. PCSK9 is primarily expressed by the liver and is critical for the down regulation of hepatocyte LDL receptor expression. LDL-C levels in plasma are highly elevated in humans with gain of function mutations in PCSK9, classifying them as having severe hypercholesterolemia. When PCSK9 binds to the LDL receptor, the receptor is broken down and can no longer remove LDL cholesterol from the blood. If PCSK9 is blocked, more LDL receptors will be present on the surface of the liver and will remove more LDL cholesterol from the blood. Therefore, PCSK9 is an attractive target for CRISPR. PCS9K-targeted CRISPR may be formulated in a lipid particle and for example administered at about 15, 45, 90, 150, 250 and 400 μg kg intraveneously (see, e.g., Clinical Study Protocol Number ALN-PCS02-001, A Phase 1, Randomized, Single-blind, Placebo-Controlled, Single Ascending Dose, Safety, Tolerability and Pharmacokinetics Study of ALN-PCS02 in Subjects with Elevated LDL-Cholesterol (LDL-C), Alnylam Pharmaceuticals, 2013).

Bailey et al. (J Mol Med (Berl). 1999 January; 77(l):244-9) discloses insulin delivery by ex-vivo somatic cell gene therapy involves the removal of non-B-cell somatic cells (e.g. fibroblasts) from a diabetic patient, and genetically altering them in vitro to produce and secrete insulin. The cells can be grown in culture and returned to the donor as a source of insulin replacement. Cells modified in this way could be evaluated before implantation, and reserve stocks could be cryopreserved. By using the patient's own cells, the procedure should obviate the need for immunosuppression and overcome the problem of tissue supply, while avoiding a recurrence of cell destruction. Ex-vivo somatic cell gene therapy requires an accessible and robust cell type that is amenable to multiple transfections and subject to controlled proliferation. Special problems associated with the use of non-B-cell somatic cells include the processing of proinsulin to insulin, and the conferment of sensitivity to glucose-stimulated proinsulin biosynthesis and regulated insulin release. Preliminary studies using fibroblasts, pituitary cells, kidney (COS) cells and ovarian (CHO) cells suggest that these challenges could be met, and that ex-vivo somatic cell gene therapy offers a feasible approach to insulin replacement therapy. The system of Bailey et al. may be used/and or adapted to the CRISPR Cas system of the present invention for delivery to the liver.

The methods of Sato et al. (Nature Biotechnology Volume 26 Number 4 Apr. 2008, Pages 431-442) may be applied to the CRISPR Cas system of the present invention for delivery to the liver. Sato et al. found that treatments with the siRNA-bearing vitamin A-coupled liposomes almost completely resolved liver fibrosis and prolonged survival in rats with otherwise lethal dimethylnitrosamine-induced liver cirrhosis in a dose- and duration-dependent manner. Cationic liposomes (Lipotrust) containing 0,0′-ditetradecanoyl-N-(a-trimethylammonioacetyl) diethanolamine chloride (DC-6-14) as a cationic lipid, cholesterol and dioleoylphosphatidylethanolamine at a molar ratio of 4:3:3 (which has shown high transfection efficiency under serumcontaining conditions for in vitro and in vivo gene delivery) were purchased from Hokkaido System Science. The liposomes were manufactured using a freeze-dried empty liposomes method and prepared at a concentration of 1 mM (DC-16-4) by addition of double-distilled water (DDW) to the lyophilized lipid mixture under vortexing before use. To prepare VA-coupled liposomes, 200 nmol of vitamin A (retinol, Sigma) dissolved in DMSO was mixed with the liposome suspensions (100 nmol as DC-16-4) by vortexing in a 1.5 ml tube at 25 1 C. To prepare VA-coupled liposomes carrying siRNAgp46 (VA-lip-siRNAgp46), a solution of siRNAgp46 (580 pmol/ml in DDW) was added to the retinol-coupled liposome solution with stirring at 25 C. The ratio of siRNA to DC-16-4 was 1:11.5 (mol/mol) and the siRNA to liposome ratio (wt/wt) was 1:1. Any free vitamin A or siRNA that was not taken up by liposomes were separated from liposomal preparations using a micropartition system (VIVASPIN 2 concentrator 30,000 MWCO PES, VIVASCIENCE). The liposomal suspension was added to the filters and centrifuged at 1,500 g for 5 min 3 times at 25 1 C. Fractions were collected and the material trapped in the filter was reconstituted with PBS to achieve the desired dose for in vitro or in vivo use. Three injections of 0.75 mg/kg siRNA were given every other day to rats. The system of Sato et al. may be used/and or adapted to the CRISPR Cas system of the present invention for delivery to the liver by delivering about 0.5 to 1 mg/kg of CRISPR Cas RNA in the liposomes as described by Sato et al. to humans.

The methods of Rozema et al. (PNAS, Aug. 7, 2007, vol. 104, no. 32) for a vehicle for the delivery of siRNA to hepatocytes both in vitro and in vivo, which Rozema et al. have named siRNA Dynamic PolyConjugates may also be applied to the present invention. Key features of the Dynamic Poly-Conjugate technology include a membrane-active polymer, the ability to reversibly mask the activity of this polymer until it reaches the acidic environment of endosomes, and the ability to target this modified polymer and its siRNA cargo specifically to hepatocytes in vivo after simple, low-pressure i.v. injection. SATA-modified siRNAs are synthesized by reaction of 5′ aminemodified siRNA with 1 weight equivalents (wt eq) of Nsuccinimidyl-S-acetylthioacetate (SAT A) reagent (Pierce) and 0.36 wt eq of NaHCC>3 in water at 4° C. for 16 h. The modified siRNAs are then precipitated by the addition of 9 vol of ethanol and incubation at 80° C. for 2 h. The precipitate is resuspended in IX siRNA buffer (Dharmacon) and quantified by measuring absorbance at the 260-nm wavelength. PBAVE (30 mg/ml in 5mMTAPS, pH 9) is modified by addition of 1.5 wt % SMPT (Pierce). After a 1-h incubation, 0.8 mg of SMPT-PBAVE was added to 400 μ

of isotonic glucose solution containing 5 mM TAPS (pH 9). To this solution was added 50 μg of SATA-modified siRNA. For the dose-response experiments where [PBAVE] was constant, different amounts of siRNA are added. The mixture is then incubated for 16 h. To the solution is then added 5.6 mg of Hepes free base followed by a mixture of 3.7 mg of CDM-NAG and 1.9 mg of CDM-PEG. The solution is then incubated for at least 1 h at room temperature before injection. CDM-PEG and CDM-NAG are synthesized from the acid chloride generated by using oxalyl chloride. To the acid chloride is added 1.1 molar equivalents polyethylene glycol monomethyl ether (molecular weight average of 450) to generate CDM-PEG or (aminoethoxy)ethoxy-2-(acetylamino)-2-deoxy-P-D-glucopyranoside to generate CDM-NAG. The final product is purified by using reverse-phase HPLC with a 0.1% TFA water/acetonitrile gradient. About 25 to 50 μg of siRNA was delivered to mice. The system of Rozema et al. may be applied to the CRISPR Cas system of the present invention for delivery to the liver, for example by envisioning a dosage of about 50 to about 200 mg of CRISPR Cas for delivery to a human.

In an aspect the invention provides methods, regents and companion diagnostics for identifying and treating subjects at risk for, or having a PCSK9 mediated disorder. The invention can be used to select therapeutic compositions and dosages, to predict and monitor responses and outcomes. Subjects may be treated to prevent, delay the onset of, or ameliorate the PCSK9 mediated disorder by promoting or inhibiting PCSK9 activity.

PCSK9 inhibitors can be used to treat patients with familial hypercholesterolemia (FH), clinical atherosclerotic cardiovascular disease (CVD), and other disorders requiring lowering of LDL cholesterol (LDL-C). PCSK9 inhibitors include evolocumab (Repatha™), Praulent, and Alnylam. Evolocumab is a human monoclonal IgG2 antibody which binds to PCSK9 and inhibits circulating PCSK9 from binding to the low density lipoprotein (LDL) receptor (LDLR), preventing PCSK9-mediated LDLR degradation and permitting LDLR to recycle back to the liver cell surface. By inhibiting the binding of PCSK9 to LDLR, evolocumab increases the number of LDLRs available to clear LDL from the blood, thereby lowering LDL-C levels. Evolocumab can be administered alone or in combination with other agents. Evolocumab is used as an adjunct to diet and maximally tolerated statin therapy.

The liver X receptor (LXR) is a member of the nuclear receptor family of transcription factors and is related to nuclear receptors such as the PPARs, FXR and RXR. Liver X receptors (LXRs) are nuclear receptors involved in regulation of lipid metabolism, cholesterol homeostasis, and inflammatory responses in the central nervous system. Two isoforms of LXR have been identified and are referred to as LXRa and LXRp. LXRa expression is restricted to liver, kidney, intestine, fat tissue, macrophages, lung, and spleen and is highest in liver, whereas LXRP is expressed in almost all tissues and organs. Defects contribute to the pathogenesis of neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, multiple sclerosis, and Huntington's disease.

In an aspect the invention provides methods, regents and companion diagnostics for identifying and treating subjects at risk for, or having an LXR mediated disorder. Subjects are identified and can be treated to prevent, delay the onset of, or ameliorate the LXR mediated disorder by activating or inhibiting LXR activity. LXR agonists (e.g., hypocholamide, T0901317, GW3965, or N,N-dimethyl-3beta-hydroxy-cholenamide (DMHCA)) are useful to reduce the cholesterol level in serum and liver and inhibits the development of atherosclerosis in murine disease models. Certain LXR agonists (e.g. GW3965) improve glucose tolerance in a model of diet-induced obesity and insulin resistance by regulating genes involved in glucose metabolism in liver and adipose tissue.

Peroxisome proliferator-activated receptors (PPARs) are a group of nuclear receptor proteins that function as transcription factors regulating the expression of genes and play essential roles in the regulation of cellular differentiation, development, and metabolism. There are three main forms: PPARa; PPARβ/δ, and PPARy. The PPARy1 isoform is expressed in the spleen, intestine, and white adipose tissue, the PPARy2 is preferentially expressed in white and brown fat, and PPARy2 is most abundantly in fat cells, and plays a pivotal role in fat cell differentiation and lipid storage. Hereditary disorders of all PPARs have been described, leading to, for example, lipodystrophy, and insulin resistance.

In an aspect, the invention provides methods, reagents and diagnostics for identifying, monitoring, and treating inherited disorders. In certain embodiments, the disorders may have single genetic components. Advantageously, the invention is useful for complex multifactorial disorders that do not have a single genetic cause, such as, but not limited to, heart disease, diabetes, and obesity. Thus, multiple gene loci can be evaluated and treatments designed or adjusted accordingly. Accordingly, a subject's disease or disorder can be diagnosed as to relative contributions of variations in multiple genes. Accordingly, patient-specific treatments cam be selected involving multiple drugs in specific combinations and/or dosages. For example, depending on the presence of gene variants at genetic loci that determine propensity to develop a metabolic disease, patient-specific combinations and dosages of drugs to treat diabetes can be selected. According to the invention, in one embodiment, a patient suffering from a particular disease is diagnosed to determine the presence or absence of genetic variants associated with a disease or condition, and a treatment regime assigned accordingly. In particular, treatments that are likely to be effective or to which a subject is likely to be most sensitive are selected. In another embodiment, treatments which are predicted to result in unwanted patient-specific side effects are avoided.

Advantageously, the invention provides methods, reagents and diagnostics for identifying, monitoring, and treating diseases or disorders that arise spontaneously or develop over time. In certain embodiments, such disorders are clonal. By clonal it is meant that there is an aspect to the disease that results from appearance or enlargement (or disappearance) of a population of cells. One non-limiting example is a clonal population of cells that arises through a spontaneous mutation. Another non-limiting example is a clonal population of cells that arises from an external stimulation. For example, an autoimmune disorder may arise or be exacerbated by expansion or activation of a population of immune cells. Yet another example is loss of a highly diverse population of cells leaving a population arising from a small number of clones. For example, it has been observed that clonal diversity of immune cells capable of mounting an immune response diminishes with age. In certain embodiments, there is activation or inhibition of expression of one or more genes that develops over time. For example, expression levels can be monitored by measuring transcription or evaluating DNA methylation.

In an embodiment of the invention, a cholesterol level is monitored and a treatment to reduce cholesterol is initiated. One or more o

DNMT3A, TET2, ASXLI, TP53, JAK2 and SF3B1 is tested for sequence and a cholesterol medication is selected accordingly, taking into account the alleles of DNMT3A, TET2, ASXLI, TP53, JAK2 and SF3B1 present in the subject and their correlation with effectiveness of treatment and minimization of undesired side effects such as liver function and muscle pain.

Provided herein are sensitive and specific methods to detect and closely monitor somatic mutations associated with disease, particularly a cardiometabolic disease and a hematological cancer. The companion diagnostic methods provided herein are based on the finding that clonal hematopoiesis due to somatic mutation is a common finding in the elderly, and most frequently involves DNMT3A, TET2, and ASXLI. This clinical entity, CHIP, is associated with increased risk of developing hematological malignancy, minimal changes in blood counts, increased overall mortality, and increased risk of cardiometabolic disease. In addition, the companion diagnostic method provided herein also is based on the finding that detecting mutations in DNMT3A, TET2, ASXLI, TP 53, JAK2 and SF3B1 specifically provides superior prognostic and treatment selection information as compared to other markers involved in cardiometabolic disease and a hematological cancer. In exemplary methods provided herein, the diagnostic and prognostic methods are companion methods to therapy with any treatment described herein.

In one embodiment, the companion diagnostic is used to monitor clonality of somatic mutations in a patient. In another embodiment, clonality is determined. Not being bound by a theory, the size of a clone harboring a somatic mutation, as described herein, can be used to monitor the effectiveness of a treatment.

In one embodiment, a patient is treated with a cholesterol lowering drug if a mutation in TET2, DNMT3A, and/or JAK2 is observed. Not being bound by a theory, TET2, DNMT3A, and/or JAK2 mutations result in a deficit of reverse cholesterol transport in macrophages and treatment with a cholesterol lowering drug described herein can ameliorate this deficit. In another embodiment, a patient is treated with an anti-inflammatory drug described herein, if a mutation in TET2, DNMT3A, and/or JAK2 is observed. Not being bound by a theory, TET2, DNMT3A, and/or JAK2 mutations result in a protracted inflammatory phenotype in macrophages and treatment with an anti-inflammatory drug described herein can ameliorate this phenotype.

The present invention also relates to identifying molecules, advantageously small molecules or biologies, that may be involved in inhibiting one or more of the mutations in one or more genes selected from the group consisting of DNMT3A, TET2, ASXLI, TP53, JAK2 and SF3B1. The invention contemplates screening libraries of small molecules or biologies to identify compounds involved in suppressing or inhibiting expression of somatic mutations or alter the cells phenotypically so that the cells with mutations behave more normally in one or more OÏ′ONMT3A, TET2, ASXLI, TP53, JAK2 and SF3B1.

High-throughput screening (HTS) is contemplated for identifying small molecules or biologies involved in suppressing or inhibiting expression of somatic mutations in one or more of DNMT3A, TET2, ASXLI, TP53, JAK2 and SF3B1. The flexibility of the process has allowed numerous and disparate areas of biology to engage with an equally diverse palate of chemistry (see, e.g., Inglese et al, Nature Chemical Biology 3, 438-441 (2007)). Diverse sets of chemical libraries, containing more than 200,000 unique small molecules, as well as natural product libraries, can be screened. This includes, for example, the Prestwick library (1,120 chemicals) of off-patent compounds selected for structural diversity, collective coverage of multiple therapeutic areas, and known safety and bioavailability in humans, as well as the NINDS Custom Collection 2 consisting of a 1,040 compound-library of mostly FDA-approved drugs (see, e.g., U.S. Pat. No. 8,557,746) are also contemplated.

The NIH's Molecular Libraries Probe Production Centers Network (MLPC) offers access to thousands of small molecules—chemical compounds that can be used as tools to probe basic biology and advance our understanding of disease. Small molecules can help researchers understand the intricacies of a biological pathway or be starting points for novel therapeutics. The Broad Institute's Probe Development Center (BIPDeC) is part of the MLPCN and offers access to a growing library of over 330,000 compounds for large scale screening and medicinal chemistry. Any of these compounds may be utilized for screening compounds involved in suppressing or inhibiting expression of somatic mutations in one or more of DNMT3A, TET2, ASXL1, TP53, JAK2 and SF3B1.

The phrase “therapeutically effective amount” as used herein refers to a nontoxic but sufficient amount of a drug, agent, or compound to provide a desired therapeutic effect.

As used herein “patient” refers to any human being receiving or who may receive medical treatment.

A “polymorphic site” refers to a polynucleotide that differs from another polynucleotide by one or more single nucleotide changes.

A “somatic mutation” refers to a change in the genetic structure that is not inherited from a parent, and also not passed to offspring.

Therapy or treatment according to the invention may be performed alone or in conjunction with another therapy, and may be provided at home, the doctor's office, a clinic, a hospital's outpatient department, or a hospital. Treatment generally begins at a hospital so that the doctor can observe the therapy's effects closely and make any adjustments that are needed. The duration of the therapy depends on the age and condition of the patient, the stage of the a cardiovascular disease, and how the patient responds to the treatment. Additionally, a person having a greater risk of developing a cardiovascular disease (e.g., a person who is genetically predisposed) may receive prophylactic treatment to inhibit or delay symptoms of the disease.

The medicaments of the invention are prepared in a manner known to those skilled in the art, for example, by means of conventional dissolving, lyophilizing, mixing, granulating or confectioning processes. Methods well known in the art for making formulations are found, for example, in Remington: The Science and Practice of Pharmacy, 20th ed., ed. A. R. Gennaro, 2000, Lippincott Williams & Wilkins, Philadelphia, and Encyclopedia of Pharmaceutical Technology, eds. J. Swarbrick and J. C. Boylan, 1988-1999, Marcel Dekker, New York.

Administration of medicaments of the invention may be by any suitable means that results in a compound concentration that is effective for treating or inhibiting (e.g., by delaying) the development of a cardiovascular disease. The compound is admixed with a suitable carrier substance, e.g., a pharmaceutically acceptable excipient that preserves the therapeutic properties of the compound with which it is administered. One exemplary pharmaceutically acceptable excipient is physiological saline. The suitable carrier substance is generally present in an amount of 1-95% by weight of the total weight of the medicament. The medicament may be provided in a dosage form that is suitable for oral, rectal, intravenous, intramuscular, subcutaneous, inhalation, nasal, topical or transdermal, vaginal, or ophthalmic administration. Thus, the medicament may be in form of, e.g., tablets, capsules, pills, powders, granulates, suspensions, emulsions, solutions, gels including hydrogels, pastes, ointments, creams, plasters, drenches, delivery devices, suppositories, enemas, injectables, implants, sprays, or aerosols.

In order to determine the genotype of a patient according to the methods of the present invention, it may be necessary to obtain a sample of genomic DNA from that patient. That sample of genomic DNA may be obtained from a sample of tissue or cells taken from that patient.

The tissue sample may comprise but is not limited to hair (including roots), skin, buccal swabs, blood, or saliva. The tissue sample may be marked with an identifying number or other indicia that relates the sample to the individual patient from which the sample was taken. The identity of the sample advantageously remains constant throughout the methods of the invention thereby guaranteeing the integrity and continuity of the sample during extraction and analysis. Alternatively, the indicia may be changed in a regular fashion that ensures that the data, and any other associated data, can be related back to the patient from whom the data was obtained. The amount/size of sample required is known to those skilled in the art.

Generally, the tissue sample may be placed in a container that is labeled using a numbering system bearing a code corresponding to the patient. Accordingly, the genotype of a particular patient is easily traceable.

In one embodiment of the invention, a sampling device and/or container may be supplied to the physician. The sampling device advantageously takes a consistent and reproducible sample from individual patients while simultaneously avoiding any cross-contamination of tissue. Accordingly, the size and volume of sample tissues derived from individual patients would be consistent.

According to the present invention, a sample of DNA is obtained from the tissue sample of the patient of interest. Whatever source of cells or tissue is used, a sufficient amount of cells must be obtained to provide a sufficient amount of DNA for analysis. This amount will be known or readily determinable by those skilled in the art.

DNA is isolated from the tissue/cells by techniques known to those skilled in the art (see, e.g., U.S. Pat. Nos. 6,548,256 and 5,989,431, Hirota et al, Jinrui Idengaku Zasshi. September 1989; 34(3):217-23 and John et al, Nucleic Acids Res. Jan. 25, 1991; 19(2):408; the disclosures of which are incorporated by reference in their entireties). For example, high molecular weight DNA may be purified from cells or tissue using proteinase K extraction and ethanol precipitation. DNA may be extracted from a patient specimen using any other suitable methods known in the art.

It is an object of the present invention to determine the genotype of a given patient of interest by analyzing the DNA from the patent, in order to identify a patient carrying specific somatic mutations of the invention that are associated with developing a cardiovascular disease. In particular, the kit may have primers or other DNA markers for identifying particular mutations such as, but not limited to, one or more genes selected from the group consisting of DNMT3A, TET2, ASXL1, TP53, JAK2 and SF3B1.

There are many methods known in the art for determining the genotype of a patient and for identifying or analyzing whether a given DNA sample contains a particular somatic mutation. Any method for determining genotype can be used for determining genotypes in the present invention. Such methods include, but are not limited to, amplimer sequencing, DNA sequencing, fluorescence spectroscopy, fluorescence resonance energy transfer (or “FRET”)-based hybridization analysis, high throughput screening, mass spectroscopy, nucleic acid hybridization, polymerase chain reaction (PCR), RFLP analysis and size chromatography (e.g., capillary or gel chromatography), all of which are well known to one of skill in the art.

The methods of the present invention, such as whole exome sequencing and targeted amplicon sequencing, have commercial applications in diagnostic kits for the detection of the somatic mutations in patients. A test kit according to the invention may comprise any of the materials necessary for whole exome sequencing and targeted amplicon sequencing, for example, according to the invention. In a particular advantageous embodiment, a companion diagnostic for the present invention may comprise testing for any of the genes in disclosed herein. The kit further comprises additional means, such as reagents, for detecting or measuring the sequences of the present invention, and also ideally a positive and negative control.

The present invention further encompasses probes according to the present invention that are immobilized on a solid or flexible support, such as paper, nylon or other type of membrane, filter, chip, glass slide, microchips, microbeads, or any other such matrix, all of which are within the scope of this invention. The probe of this form is now called a “DNA chip”. These DNA chips can be used for analyzing the somatic mutations of the present invention. The present invention further encompasses arrays or microarrays of nucleic acid molecules that are based on one or more of the sequences described herein. As used herein “arrays” or “microarrays” refers to an array of distinct polynucleotides or oligonucleotides synthesized on a solid or flexible support, such as paper, nylon or other type of membrane, filter, chip, glass slide, or any other suitable solid support. In one embodiment, the microarray is prepared and used according to the methods and devices described in U.S. Pat. Nos. 5,446,603; 5,545,531; 5,807,522; 5,837,832; 5,874,219; 6,114,122; 6,238,910; 6,365,418; 6,410,229; 6,420,114; 6,432,696; 6,475,808 and 6,489,159 and PCT Publication No. WO 01/45843 A2, the disclosures of which are incorporated by reference in their entireties.

For the purposes of the present invention, sequence identity or homology is determined by comparing the sequences when aligned so as to maximize overlap and identity while minimizing sequence gaps. In particular, sequence identity may be determined using any of a number of mathematical algorithms. A nonlimiting example of a mathematical algorithm used for comparison of two sequences is the algorithm of Karlin & Altschul, Proc. Natl. Acad. Sci. USA 1990; 87: 2264-2268, modified as in Karlin & Altschul, Proc. Natl. Acad. Sci. USA 1993; 90: 5873-5877.

Another example of a mathematical algorithm used for comparison of sequences is the algorithm of Myers & Miller, CABIOS 1988; 4: 11-17. Such an algorithm is incorporated into the ALIGN program (version 2.0) which is part of the GCG sequence alignment software package. When utilizing the ALIGN program for comparing amino acid sequences, a PAM120 weight residue table, a gap length penalty of 12, and a gap penalty of 4 can be used. Yet another useful algorithm for identifying regions of local sequence similarity and alignment is the FASTA algorithm as described in Pearson & Lipman, Proc. Natl. Acad. Sci. USA 1988; 85: 2444-2448.

Advantageous for use according to the present invention is the WU-BLAST (Washington University BLAST) version 2.0 software. WU-BLAST version 2.0 executable programs for several UNIX platforms can be downloaded from the FTP site for Blast at the Washington University in St. Louis website. This program is based on WU-BLAST version 1.4, which in turn is based on the public domain NCBI-BLAST version 1.4 (Altschul & Gish, 1996, Local alignment statistics, Doolittle ed., Methods in Enzymology 266: 460-480; Altschul et al., Journal of Molecular Biology 1990; 215: 403-410; Gish & States, 1993; Nature Genetics 3: 266-272; Karlin & Altschul, 1993; Proc. Natl. Acad. Sci. USA 90: 5873-5877; all of which are incorporated by reference herein).

In all search programs in the suite the gapped alignment routines are integral to the database search itself. Gapping can be turned off if desired. The default penalty (Q) for a gap of length one is Q=9 for proteins and BLASTP, and Q=10 for BLASTN, but may be changed to any integer. The default per-residue penalty for extending a gap (R) is R=2 for proteins and BLASTP, and R=10 for BLASTN, but may be changed to any integer. Any combination of values for Q and R can be used in order to align sequences so as to maximize overlap and identity while minimizing sequence gaps. The default amino acid comparison matrix is BLOSUM62, but other amino acid comparison matrices such as PAM can be utilized.

Alternatively or additionally, the term “homology” or “identity”, for instance, with respect to a nucleotide or amino acid sequence, can indicate a quantitative measure of homology between two sequences. The percent sequence homology can be calculated as (N_(ref)−Ndi_(f))*100/−N_(ref), wherein Ndi_(f) is the total number of non-identical residues in the two sequences when aligned and wherein N_(ref) is the number of residues in one of the sequences. Hence, the DNA sequence AGTCAGTC will have a sequence identity of 75% with the sequence AATCAATC (N N_(ref)=8; N Ndi_(f)=2). “Homology” or “identity” can refer to the number of positions with identical nucleotides or amino acids divided by the number of nucleotides or amino acids in the shorter of the two sequences wherein alignment of the two sequences can be determined in accordance with the Wilbur and Lipman algorithm (Wilbur & Lipman, Proc Natl Acad Sci USA 1983; 80:726, incorporated herein by reference), for instance, using a window size of 20 nucleotides, a word length of 4 nucleotides, and a gap penalty of 4, and computer-assisted analysis and interpretation of the sequence data including alignment can be conveniently performed using commercially available programs (e.g., Intelligenetics™ Suite, Intelligenetics Inc. CA). When RNA sequences are said to be similar, or have a degree of sequence identity or homology with DNA sequences, thymidine (T) in the DNA sequence is considered equal to uracil (U) in the RNA sequence. Thus, RNA sequences are within the scope of the invention and can be derived from DNA sequences, by thymidine (T) in the DNA sequence being considered equal to uracil (U) in RNA sequences. Without undue experimentation, the skilled artisan can consult with many other programs or references for determining percent homology.

The invention further encompasses kits useful for screening nucleic acids isolated from one or more patients for any of the somatic mutations described herein and instructions for using the oligonucleotide to detect variation in the nucleotide corresponding to one or more of the somatic mutations, such as but not limited to, one or more genes selected from the group consisting of DNMT3A, TET2, ASXL1, TP53, JAK2 and SF3B1, of the isolated nucleic acid.

Another aspect of the invention is a method of screening patients to determine those patients more likely to develop a cardiovascular disease comprising the steps of obtaining a sample of genetic material from a patient; and assaying for the presence of a genotype in the patient which is associated with developing cardiovascular diseases, any of the herein disclosed somatic mutations.

In other embodiments of this invention, the step of assaying is selected from the group consisting of: restriction fragment length polymorphism (RFLP) analysis, minisequencing, MALD-TOF, SINE, heteroduplex analysis, single strand conformational polymorphism (SSCP), denaturing gradient gel electrophoresis (DGGE) and temperature gradient gel electrophoresis (TGGE).

The present invention also encompasses a transgenic mouse which may express one or more of the herein disclosed somatic mutations. Methods for making a transgenic mouse are well known to one of skill in the art, see e.g., U.S. Pat. Nos. 7,709,695; 7,667,090; 7,655,700; 7,626,076; 7,566,812; 7,544,855; 7,538,258; 7,495,147; 7,479,579; 7,449,615; 7,432,414; 7,393,994; 7,371,920; 7,358,416; 7,276,644; 7,265,259; 7,220,892; 7,214,850; 7,186,882; 7,119,249; 7,112,715; 7,098,376; 7,045,678; 7,038,105; 6,750,375; 6,717,031; 6,710,226; 6,689,937; 6,657,104; 6,649,811; 6,613,958; 6,610,905; 6,593,512; 6,576,812; 6,531,645; 6,515,197; 6,452,065; 6,372,958; 6,372,957; 6,369,295; 6,323,391; 6,323,390; 6,316,693; 6,313,373; 6,300,540; 6,255,555; 6,245,963; 6,215,040; 6,211,428; 6,201,166; 6,187,992; 6,184,435; 6,175,057; 6,156,727; 6,137,029; 6,127,598; 6,037,521; 6,025,539; 6,002,067; 5,981,829; 5,936,138; 5,917,124; 5,907,078; 5,894,078; 5,850,004; 5,850,001; 5,847,257; 5,837,875; 5,824,840; 5,824,838; 5,814,716; 5,811,633; 5,723,719; 5,720,936; 5,688,692; 5,631,407; 5,620,881; 5,574,206 and 5,569,827. The transgenic mouse may be utilized to mimic cardiovascular disease conditions and may be useful to test novel treatments of cardiovascular disease in a mouse model.

Although the present invention and its advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined in the appended claims.

The present invention will be further illustrated in the following Examples which are given for illustration purposes only and are not intended to limit the invention in any way.

EXAMPLES Example 1: Methods

Sample ascertainment. Subjects were ascertained from 22 population-based cohorts in 3 consortia (see Supplementary Table SI). These studies were performed using protocols approved by the ethics committees of all involved institutions, as well as with informed consent from all participants. Samples with missing age (116 subjects) or cell lines as the source of DNA (492 subjects) were excluded.

Whole Exome Sequencing and Targeted Amplicon Sequencing. DNA was obtained from individual cohorts and further processed at the Broad Institute of MIT and Harvard. Briefly, genomic DNA was subject to hybrid capture, sequencing, and alignment using the Broad Genomics Platform and Picard pipeline. BAM files were analyzed for SNVs using MuTect with OxoG filtering and indels using Indelocator (Cibulskis K et al. Nature biotechnology 2013; 31:213-9, Costello M et al. Nucleic acids research 2013; 41:e67). A clinically validated, targeted amplicon assay was used for sequencing of 95 genes in select samples.

Variant Calling

Applicants defined a list of pathogenic variants reported in the literature and/or the Catalog of Somatic Mutations In Cancer (COSMIC; hosted by the Wellcome Sanger Institute (WSI)) in human hematologic malignancies from 160 genes (see Lawrence, M S, et al. Nature 2014; 505:495-501, Supplementary Table S3). As a negative control, Applicants also searched for variants that were recurrently seen in non-hematologic malignancies (see Lawrence, M S, et al. Nature 2014; 505:495-501, Supplementary Table S5).

Statistics

All statistical analysis was performed using “R”, a free software environment for statistical computing provided on the internet by the R Foundation.

Example 2: Results

Identification of candidate somatic mutations. To investigate the extent of clonal hematopoiesis with somatic mutation, Applicants analyzed whole exome sequencing data from peripheral blood cell DNA of 17,182 individuals who were selected without regard to hematological characteristics. Of these, 15,801 were cases and controls ascertained from 22 cohorts for type 2 diabetes (T2D) association studies, and the remaining 1,381 were additional, previously un-sequenced individuals from the Jackson Heart Study, a population-based cohort (Supplementary Table SI). The median age of individuals was 58 (range 19-108), 8,741 were women, and 7,860 had T2D.

The identification of somatic driver mutations in cancer has come largely from studies that have compared differences in DNA sequence between tumor and normal tissue from the same individual. Once mutations are identified, investigators may genotype samples for these somatic variants without relying on a matched normal tissue. Because Applicants had DNA from only one source (blood), Applicants limited Applicants' examination to variants previously described in the literature for 160 recurrently mutated candidate genes in myeloid and lymphoid malignancies (Supplementary Table S2). Potential false-positives were removed by utilizing variant-calling algorithms with filters for known artifacts such as strand-bias and clustered reads, as well as additional filtering for rare error modes using a panel of normal (Cibulskis K et al. Nature biotechnology 2013; 31:213-9). The lower limit of detection for variants depended on the depth of coverage. The median average sequencing depth over exons from the 160 genes was 84X, and ranged from 13 to 144. At a sequencing depth of 84X, the limit of detection for SNVs was at a variant allele fraction (VAF) of 0.035; for indels, the limit was 0.07.

With this approach, Applicants identified a total of 805 candidate somatic variants (hereafter referred to as mutations) from 746 individuals in 73 genes (Supplementary Table S3 (Supplementary Table S3 discloses SEQ ID NOS 38, 38, 39, 39-46, respectively, in order of appearance)). As a negative control, Applicants searched for previously described, cancer-associated variants in 40 non-hematologic genes (Supplementary Table S4) (Lawrence M S et al. Nature 2014; 505:495-501) and found only 10 such variants in these genes. Below, Applicants show that the frequency of apparent mutations is exceedingly low in young people, and rises with age. These internal controls indicate that the rate of false discovery due to technical artifacts is low. Applicants also verified a subset of the variants using amplicon-based, targeted sequencing; 18/18 variants were confirmed with a correlation coefficient of 0.97 for the VAF between the two methods (FIG. 14A).

The frequency of clonal somatic mutation increases with age. Hematological malignancies, as well as other cancers and pre-malignant states, increase in frequency with age. Mutations were very rare in samples collected before the age of 40, but rose in frequency with each decade of life thereafter (FIG. 1). Mutations in genes implicated in hematological malignancies were found in 5.6% (95% CI 5.0-6.3%) of individuals age 60-69, 9.5% (95% CI 8.4-10.8%) of individuals age 70-79, 11.7% (95% CI 8.6-15.7%) of individuals age 80-89, and 18.4% (95% CI 12.1-27.0%) of individuals older than 90. These rates greatly exceed the incidence of clinically diagnosed hematologic malignancy in the general population (Surveillance, Epidemiology, and End Results (SEER) Program Populations (1969-2012), National Cancer Institute, DCCPS, Surveillance Research Program, Surveillance Systems Branch. 2014).

Though Applicants searched for mutations in genes implicated in multiple hematologic malignancies, Applicants primarily identified genes that were most frequently mutated in acute myeloid leukemia (AML) and myelodysplasia syndrome (MDS). The most commonly mutated gene was DNMT3A (403 variants, FIG. 2A, FIG. 6), followed by TET2 (72 variants) and ASXL1 (62 variants). Of note, only exon 3 of TET2 was baited by exon capture (corresponding to −50% of the coding region), and the portion of exon 12 of ASXL1 that accounts for ˜50%> of the mutations in this gene had poor coverage depth. Thus, mutations in TET2 and ASXL1 are likely underrepresented in this study. Other frequently mutated genes included TP53 (33 variants), JAK2 (31 variants), and SF3B1 (27 variants).

In sequencing studies of MDS and AML, most patients have mutations in 2 or more driver genes (the median number of recurrently mutated genes in de novo AML patients is five (Cancer Genome Atlas Research N. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. The New England journal of medicine 2013; 368:2059-74)). In this study, Applicants found that 693 of 746 individuals with a detectable mutation had only 1 mutation in the set of genes Applicants examined, consistent with the hypothesis that these subjects had pre-malignant clones harboring only the initiating lesion (FIG. 2B, FIG. 7A-B).

The most common base pair change in the somatic variants was cytosine to thymine (C-T) transition (FIG. 2C), which is considered to be a somatic mutational signature of aging (Welch J S et al. Cell 2012; 150:264-78, Alexandrov L B et al. Nature 2013; 500:415-21). The median VAF for the identified mutations was 0.09, (FIG. 2D), suggesting that they are present in only a subset of blood cells, and supports their somatic, rather than germline, origin.

Somatic mutations persist over time. Blood cell DNA obtained 4 to 8 years after the initial DNA collection was available for targeted sequencing in 13 subjects with 17 somatic mutations (4 subjects had 2 mutations). In all cases, the mutations detected at the earlier time point were still present at the later time point. For 10 mutations, the VAF stayed the same or slightly decreased, and for 7 mutations, the VAF clearly increased. New mutations were detected in 2 subjects, but no subjects were diagnosed with hematological malignancies (FIG. 14B).

Risk factors associated with somatic mutation. To understand risk factors that contributed to having a detectable mutation, Applicants performed a multivariable logistic regression that included age, sex, T2D status, and ancestry as covariates (Supplementary Tables S6-7 and FIG. 8). As expected, age was the largest contributor to risk of having a mutation. MDS is characterized by a slight male preponderance. In those age 60 or older, men had an increased likelihood of having a detectable mutation compared to women (OR 1.3, 95% CI 1.1-1.5, p=0.005 by logistic regression). Hispanics and Native Americans are reported to have significantly lower incidence of MDS than other groups in the United States (Rollison D E et al. Blood 2008; 112:45-52). Applicants found that Hispanics had a lower risk of having a mutation relative to those of European ancestry, whereas other groups were not significantly different (Supplementary Table S6 and FIG. 8). Of the genes Applicants queried, the spectrum of mutations did not differ between ancestry groups (FIG. 9).

Association of somatic mutation with risk of hematologic malignancy. Pre-malignant states such as MGUS, MBL, and others are associated with an increased risk of subsequently developing a malignancy. Of the cohorts that contributed data to the study, two had longitudinal follow-up information on cancer that arose subsequent to DNA collection (JHS and MEC). Together, these comprised 3,342 subjects, including 134 (4%) in whom Applicants detected somatic mutations in the blood. In a median follow-up period of 95 months, 16 hematological malignancies were reported, of which 5 (31%) were in the group that had detectable mutations (Supplementary Table S11).

In a fixed-effects meta-analysis of the 2 cohorts adjusted for age, sex, and T2D, hematological malignancies were 11-fold more common in individuals with a detectable clone (95% o CI 3.9-33), a difference that was highly significant (p<0.001). In individuals with a VAF greater than 0.10 (indicating a higher proportion of cells in the blood carrying the mutation), the risk of developing a diagnosed hematological malignancy was nearly 50-fold (HR 49, 95% CI 21-120, p<0.001) (FIG. 3A). Consistent with this finding, individuals with a mutation who went on to be diagnosed with a hematological malignancy had a significantly higher mean VAF at the time of blood collection than those who did not (25.2% vs 12.0%, p=0.003 by Wilcoxon rank sum test, FIG. 3C).

While individuals with detectable mutations had a markedly increased risk of developing hematological malignancy, the absolute risk remained small; overall, approximately 4%) of individuals with a clone developed a hematological malignancy during the study period (FIG. 3B). This translates to a risk of developing hematological malignancy of −0.5% per year overall, and ˜1% per year in those with VAF>0.10.

Blood cell indices of individuals with somatic mutations. Somatic mutations found in MDS and AML lead to abnormal differentiation, ineffective hematopoiesis, and cytopenias. Blood count data was available on 3,107 individuals from 5 cohorts (JHS, UA non-diabetic controls, Botnia, Malmo-sib, Helsinki-sib), including 139 subjects with a detectable mutation. When looking at individuals with single mutations (ASXL1, DNMT3A, JAK2, SF3B1, and TET2) or mutations in more than 1 gene versus those with no mutations, Applicants found no significant differences in mean white blood cell count, hemoglobin, platelet count, or white blood cell differential after accounting for age and sex (FIG. 10). The only statistically significant difference in blood cell indices was an increase in red blood cell distribution width (RDW), a parameter describing the variation in size of red blood cells (13.8% vs 13.4%, p=0.002 by Wilcoxon rank-sum test, Supplementary Table S8). This finding suggests that those carrying somatic mutations might have perturbations in hematopoiesis similar to those seen in MDS, even in the absence of cytopenias.

Applicants also asked whether the presence of a mutation was associated with increased likelihood of having abnormally low blood counts (Supplementary Table S9). Most of those with a mutation had no cytopenias, nor did they have a higher rate of any single cytopenia than those without mutations. A small fraction of subjects had multiple cytopenias, and these were enriched in the fraction with mutations (OR 3.0, p=0.037 by Fisher's exact test). Furthermore, among anemic subjects, those with mutations had a higher fraction of unexplained anemias than those without mutations (Supplementary Table S10).

Association of somatic clones with overall survival. Applicants next assessed whether the presence of a somatic mutation had an effect on overall survival based on available data from 5,132 individuals in 7 cohorts (FIG. 4) with a median follow-up period of 96 months. In a model adjusted for age, sex, and T2D, carrying a mutation was associated with increased all-cause mortality (HR 1.4, p=0.018 by fixed-effects meta-analysis with beta-coefficients derived from Cox proportional hazards models for individual cohorts, FIG. 4A). Kaplan-Meier survival analysis within subjects 70 or older showed an increased risk of death in those with a mutation (p=0.002 by rank-sum test, FIG. 4B and FIG. 11). Death from hematologic neoplasms alone could not account for the observed increase in mortality, as only 1 individual with a mutation died from hematological malignancy. When Applicants performed a cause-specific mortality analysis, Applicants found that those with mutations had a higher risk of death due to cardiovascular causes, but not cancer (FIG. 15).

Because Applicants found that the presence of a somatic mutation was significantly associated with higher RDW, Applicants also examined whether harboring mutations was synergistic with elevated RDW for risk of death. High RDW has been associated with increased all-cause mortality in the aging and critically ill population (Patel K V et al. Archives of internal medicine 2009; 169:515-23, Perlstein T S et al. Archives of internal medicine 2009; 169:588-94, Bazick H S et al. Critical care medicine 2011; 39: 1913-21), but the mechanism behind this association is uncertain. Information on RDW was available on 2,409 subjects in 2 cohorts. In an analysis adjusted for age, sex, and T2D status, Applicants found that having a mutation in conjunction with an RDW>14.5% (the upper limit of normal) was associated with a marked increase in the risk of death compared to those without mutations and normal RDW (HR 3.7, p<0.001, by fixed-effects meta-analysis with beta-coefficients estimated from Cox models for the two cohorts). In contrast, those with no mutation and high RDW had a more modest increase in mortality (FIG. 4C, FIG. 12).

Association of somatic clones with cardiometabolic disease. A recent paper reported that large, somatic chromosomal alterations in peripheral blood cells were associated with having T2D (Bonnefond A et al. Nature genetics 2013; 45: 1040-3). Applicants also found that somatic mutations in genes known to cause hematologic malignancies were significantly associated with increased risk of T2D, even after adjustment for potential confounding variables (OR 1.3, p<0.001, FIGS. 11, 12). Those with T2D were slightly more likely to have mutations than those without T2D at each age group (FIG. 8).

Cardiovascular disease is the leading cause of death worldwide. Given the association of somatic mutations with all-cause mortality beyond that explicable by hematologic malignancy and T2D, Applicants performed association analyses from two cohorts comprising 3,353 subjects with available data on coronary heart disease (CHD) and ischemic stroke (IS). After excluding those with prevalent events, Applicants found that those carrying a mutation had increased cumulative incidence of both CHD and IS (FIGS. 5A and 5B). In multivariable analyses that included age, sex, T2D, systolic blood pressure, and body mass index as covariates, the hazard ratio of incident CHD and IS was 2.0 (95% CI 1.2-3.5, P=0.015) and 2.6 (95% CI 1.3-4.8, P=0.003) in the individuals carrying a somatic mutation as compared to those without (FIGS. 5C and 5D, FIG. 8).

For a subset of individuals, the traditional risk factors of smoking, total cholesterol, and high-density lipoprotein were also available; the presence of a somatic mutation remained significantly associated with incident CHD and IS even in the presence of these risk factors, and the risk was even greater in those with VAF>0.10 (Supplementary Table S12). Elevated RDW and high-sensitivity C-reactive protein (hsCRP) have also been associated with adverse cardiac outcomes (Tonelli M et al. Circulation 2008; 117: 163-8, Ridker P M et al. The New England journal of medicine 2002; 347: 1557-65), possibly reflecting an underlying inflammatory cause. In a multivariable analysis of 1,795 subjects from JHS, those with a mutation and RDW>14.5% had a markedly increased risk of incident CHD, and this effect was independent of hsCRP (Supplementary Table SI 3).

Further validation was performed showing the relationship between clonal hematopoiesis and risk for cardiovascular disease by analyzing two additional cohort studies. The BioImage Study is a study of the characteristics of subclinical cardiovascular disease, as measured by imaging modalities, unsupervised circulating biomarker measurements, and risk factors that predict progression to overt clinical cardiovascular disease, in a diverse, population-based sample of 7,300 men (aged 55-80) and women (aged 60-80). The socio-demographics of the study population aims to mirror the US population as a whole with approximately 69% of the cohort will be white, 12% African-American, 13% Hispanic, 4% Asian, predominantly of Chinese descent and 2% other (U.S. Census Bureau: 2000). The Malmo Diet and Cancer study is a 10-year prospective case-control study in 45-64-year-old men and women (n=53,000) living in a city with 230,000 inhabitants.

Participants from the nested case-control studies that passed sample quality control (contamination, prevalent cardiovascular disease, germline Ti/Tv, germline total variants/depth, germline F inbreeding coefficient) are displayed (Table 1). Participants were matched by age, sex, diabetes status, and smoking status. LDL cholesterol and total cholesterol are adjusted accounting for statin medications as previously described. Individuals that carried at least one mutation in a gene conferring risk of clonal expansion with a variant allele fraction >0.10 are defined as carrying a somatic clone.

TABLE 1 Baseline characteristics of study participants BioImage Malmo Diet & Cancer Cases Controls Cases Controls N = 150 N = 344 N = 536 N = 531 Age, y 70.2 (5.8) 70.3 (5.9) 59.9 (5.4) 59.9 (5.4) Female 60 (42.9%) 136 (39.5%) 233 (43.5%) 233 (43.9%) Total cholesterol, mg/dL 208.8 (38.1) 214.0 (37.5) 271.2 (173.1) 252.7 (133.8) LDL cholesterol, mg/dL 127.4 (31.4) 122.9 (32.8) 186.9 (117.1) 172.0 (92.5) HDL cholesterol, mg/dL 51.0 (14.7) 53.1 (15.1) 49.1 (13.0) 51.8 (13.7) Triglycerides, mg/dL 181.6 (94.1) 169.6 (93.6) 127.4 (57.4) 123.7 (58.9) Diabetes mellitus type 2 36 (24.0%) 89 (25.9%) 82 (15.3%) 80 (15.1%) Hypertension 130 (86.7%) 256 (74.4%) 421 (78.5%) 354 (66.7%) Smoker 24 (16.0%) 55 (16.0%) 167 (31.2%) 166 (31.3%) BMI 27.8 (4.7) 27.4 (4.7) 26.5 (4.0) 26.2 (4.0) Somatic clone carrier 24 (16.0%) 34 (9.9%) 31 (5.8%) 22 (4.1%)

Applicants show an increased risk of cardiovascular events from carrying a somatic clonal mutation in both studies (FIG. 18). Furthermore, Applicants show that an increase in coronary arterial calcification quantity is associated with somatic clonal mutation carrier status (FIG. 19).

Example 3: Discussion

Applicants find that somatic mutations leading to clonal outgrowth of hematopoietic cells are frequent in the general population. This entity, which Applicants term clonal hematopoiesis with indeterminate potential (CHIP), is present in over 10% of individuals over 70, making it one of the most common known pre-malignant lesions. The exact prevalence of CHIP is dependent on how cancer-causing mutations are defined and on the sensitivity of the technique used to detect mutations, and thus may substantially exceed this estimate. Unlike other pre-malignant lesions, CHIP appears to involve a substantial proportion of the affected tissue in most individuals; based on the proportion of alleles with the somatic mutation, Applicants find that a median of 18% of peripheral blood leukocytes are part of the abnormal clone. CHIP also persists over time; in all tested cases, the mutations were still present after 4 to 8 years.

The genes most commonly mutated in CHIP are DNMT3A, TET2, and ASXL1. This is consistent with previous studies that have found DNMT3A and TET2 mutations to be frequent and early events in AML and MDS (Jan M et al. Science translational medicine 2012; 4: 149ral8, Shlush L I et al. Nature 2014; 506:328-33, Papaemmanuil E et al. The New England journal of medicine 2011; 365: 1384-95, Welch J S et al. Cell 2012; 150:264-78). Murine models of DNMT3A or TET2 loss-of-function demonstrate that mutant HSCs have altered methylation patterns at pluripotency genes and a competitive advantage compared to wild-type HSCs, but mice rarely develop frank malignancy, and then only after long latency (Jeong M et al. Nature genetics 2014; 46:17-23, Koh K P et al. Cell stem cell 2011; 8:200-13, Challen G A et al. Nature genetics 2012; 44:23-31, Moran-Crusio K et al. Cancer cell 2011; 20: 11-24). Similarly, Applicants' data show that humans with CHIP can live for many years without developing hematological malignancies, though they do have increased risk relative to those without mutations.

Certain genes commonly mutated in AML and MDS are absent or very rare in this study. Their rarity likely indicates that they are cooperating rather than initiating mutations. While mutations in genes specific for lymphoid malignancies were rarely detected, it is important to note that TET2 and DNMT3A are frequently mutated in some lymphoid malignancies, and the initiating event for such tumors may occur in a HSC (Neumann M et al. Blood 2013; 121:4749-52, Quivoron C et al. Cancer cell 2011; 20:25-38, Odejide O et al. Blood 2014; 123: 1293-6, Asmar F et al. Haematologica 2013; 98: 1912-20, Couronne L et al. The New England journal of medicine 2012; 366:95-6). While it is most likely that these mutations occur in a HSC, it also possible that they occur in committed myeloid progenitors or mature lymphoid cells that have acquired long-term self-renewal capacity.

The use of somatic mutations to aid in the diagnosis of patients with clinical MDS is becoming widespread. Applicants' data demonstrate that the majority of individuals with clonal mutations in peripheral blood do not have MDS or another hematological malignancy, nor do the majority develop a clinically diagnosed malignancy in the near term. At this time, it would be premature to genetically screen healthy individuals for the presence of a somatic clone, as the positive predictive value for current or future malignancy is low. Further studies are needed to definitively assess whether the detection of a mutation in conjunction with blood count abnormalities is sufficient to make a presumptive diagnosis of MDS or another hematological malignancy.

Perhaps the most surprising finding in Applicants' study is the lower overall rate of survival in those with clones as compared to those without. This effect is much larger than can be explained by hematological malignancies alone, is synergistic with high RDW (which could be a marker of perturbation of hematopoiesis due to the clone), and may be related to the increased risk of incident CHD and IS in those with clones. The association of somatic mutations with non-hematological disease may be due to confounding by variables that are currently unknown, or may simply represent a shared consequence of the underlying process of aging. Alternatively, it may represent an underlying shared pathophysiology of seemingly unrelated disorders. For example, cells of the monocyte/macrophage lineage are considered important mediators of atherosclerosis and type 2 diabetes (Libby P. 2002; 420:868-74, Olefsky J M, Glass C K. Annual review of physiology 2010; 72:219-46). Applicants propose that one possible explanation for these findings is that somatic mutations that lead to clonal hematopoiesis cause functional abnormalities in differentiated blood cells that modulate the risk of cardiometabolic disease.

In summary, Applicants find that clonal hematopoiesis due to somatic mutation is a common finding in the elderly, and most frequently involves DNMT3A, TET2, and ASXL1. This clinical entity, CHIP, is associated with increased risk of developing hematological malignancy, minimal changes in blood counts, increased overall mortality, and increased risk of cardiometabolic disease.

Example 4: Supplemental Methods

Sample ascertainment and cohort descriptions. Subjects were ascertained as cases and controls for T2D from 22 cohorts in 3 consortia (Supplementary Table 1). Details on sample ascertainment for cases and controls from the most of the GoT2D (8 cohorts, 2,376 subjects) and SIGMA (4 cohorts, 3,435 subjects) consortia have been previously described (Flannick J et al. Nature genetics 2013; 45: 1380-5, Consortium STD et al. Nature 2014; 506:97-101). The other ascertained individuals were part of T2D-GENES (9,990 subjects), a consortium comprised of 10 population-based cohorts. Details on sample ascertainment can be found in Supplementary Table 1. The remaining 1,381 individuals were additional subjects in the Jackson Heart Study (JHS), a large population-based cohort of African-Americans in Jackson, Miss., who had given consent for genetic testing but were not in any previously sequenced cohorts (Sempos C T et al. The American journal of the medical sciences 1999; 317: 142-6). Including the 1,027 subjects from JHS enrolled through T2D-GENES (513 with T2D), a total of 2,408 subjects from JHS were in this study. Since 3,400 subjects in JHS were consented for genetic studies, −70% of consented subjects from JHS are represented in this study, with a modest overall enrichment for T2D.

Subjects for which age was not available (116 subjects) or with cell lines as the source DNA (492 subjects) were excluded, including all subjects from the Wellcome Trust Case Control Consortium (WTCCC).

Vital status for Finland-United States Investigation of NIDDM Genetics Study (FUSION), The Botnia Study (Botnia), Helsinki Siblings with Diabetes cohort (Helsinki sib), and Scania Diabetes Register (Diabetes reg) was ascertained from the Finnish or Swedish Hospital Death Registries. Subjects from Botnia, Helsinki sib, and Diabetes reg were pooled for survival analysis. Vital status for subjects from the Multiethnic Cohort (MEC) was ascertained from Center for Medicare Services (CMS) data. Vital status for subjects from the JHS was ascertained from vital records and annual follow-up interviews. For individuals lost to follow-up, if there was no death certificate, the individual was assumed to be alive. Vital status for non-diabetic Ashkenazis in the Longevity Genes Project (LGP) was ascertained from hospital death records and annual follow-up interviews.

Malignancy information for MEC was ascertained through linkage of the MEC with cancer registries of California and Hawaii. Malignancy information for JHS was ascertained from annual interview. Malignancy information for some subjects from FUSION and Botnia was available from the Finnish Hospital Discharge Register and Death Register, but not included because it was deemed to have significant ascertainment bias.

Standard blood cell indices (white blood cell count, hemoglobin, hematocrit, platelet count, and white blood cell differential) were available for most (but not all) subjects from JHS, LGP, Botnia, Malmo-sib, and Helsinki-sib. Information on red blood cell distribution width (RDW) was available on most subjects in JHS and LGP.

Data on cardiovascular outcomes for JHS was obtained from annual patient interview and adjudicated from hospital records. Data on cardiovascular outcomes for FUSION was obtained from Finnish Hospital Discharge and Death Registries. Coronary heart disease (CHD) included fatal and non-fatal myocardial infarctions as well as coronary revascularization procedures. For CHD analysis, those with prior CHD events were excluded. For ischemic stroke analysis, those with prior ischemic stroke were excluded. Lab data (blood pressure, body mass index, serum high density lipoprotein, serum total cholesterol, and high-sensitivity C-reactive protein) was obtained at the same time as blood collection for DNA.

Exome sequencing. DNA was obtained from individual cohorts and further processed at the Broad Institute of MIT and Harvard. DNA libraries were bar coded using the Illumina index read strategy, exon capture was performed using Agilent Sure-Select Human All Exon v2.0, and sequencing was performed by Illumina HiSeq2000. Sequence data were aligned by the Picard pipeline, as hosted on line by SourceForge, using reference genome hg19 with the BWA algorithm (Li H, Durbin R. Bioinformatics 2009; 25:1754-60) and processed with the Genome Analysis Toolkit (GATK) to recalibrate base-quality scores and perform local realignment around known insertions and deletions (indels) (DePristo M A et al. Nature genetics 2011; 43:491-8). BAM files were then analyzed for single nucleotide variants using MuTect (available online and hosted by The Cancer Genome Analysis (CGA) group at the Cancer Program of the Broad Institute; see Cibulskis, K. et al. Nat Biotechnol 2013; 31, 213-219) with Oxo-G filtering (available online and hosted by The Cancer Genome Analysis (CGA) group at the Cancer Program of the Broad Institute; see Costello et al., 2013 Nucleic Acids Research, 41(6), e67) and for indels using Indelocator (a software tool for calling short indels in next generation sequencing data available online and hosted by The Cancer Genome Analysis (CGA) group at the Cancer Program of the Broad Institute), followed by annotation using Oncotator (a tool for annotating human genomic point mutations and indels with data relevant to cancer researchers available online and hosted by The Cancer Genome Analysis (CGA) group at the Cancer Program of the Broad Institute; see Ramos et al., Human Mutation 2015, 36(4), E2423-E2429) ( ). All MuTect and Indelocator analyses were performed using the Firehose analysis infrastructure developed and hosted at The Cancer Genome Analysis (CGA) group at the Cancer Program of the Broad Institute.

Variant calling. Cancer genome studies typically compare sequence from tumor and germline DNA, and define somatic mutations as the sequence variants present in tumor but not germline DNA. To circumvent the lack of matched tissue in this study, Applicants defined a list of pathogenic variants reported in the literature and/or the Catalog of Somatic Mutations in Cancer (COSMIC) in human hematologic malignancies from 160 genes (see Lawrence, M S, et al. Nature 2014; 505:495-501, Supplementary Table S2). Applicants specifically excluded genes known to be involved in hematologic malignancy that had a relatively high frequency of heterozygous loss-of-function germline mutations in the population (NF1, SH2B3, BRCA1/2). Identical frameshift variants that were seen 3 or more times from the same ancestry group were also excluded, unless such variants were previously reported as somatic. Frameshift and nonsense mutations were further excluded if they occurred in the first or last 10% of the gene open reading frame (Ng P C et al. PLoS genetics 2008; 4:e1000160), unless mutations in those regions had been previously reported, (e.g. DNMT3A (Walter M J et al. Leukemia 2011; 25:1153-8)). Applicants used minimum variant read counts of 3 for MuTect and 6 for Indelocator. Python scripts were used to parse mutation annotation format (MAF) files produced by MuTect and Indelocator for variants of interest.

To further confirm that Applicants were detecting bona fide somatic mutations, Applicants examined variants in 40 driver genes involved in non-hematologic malignancies (Lawrence M S et al. Nature 2014; 505:495-501). Applicants hypothesized that very few variants would be detected from these genes if Applicants' methodology had high specificity for real mutations. Using the same calling approach as with hematologic genes (Supplementary Table S4), Applicants detected only 10 variants that were the same as those mutated in non-hematologic cancers, and most of these appeared to be rare germline polymorphisms as evidenced by allele fraction (Supplementary Table S5).

Targeted re-sequencing. Validation of variants discovered by whole exome sequencing was done with “Rapid Heme Panel” (RHP), a Laboratory Developed Test designed and validated at a CLIA-certified lab (Center for Advanced Molecular Diagnostics, Brigham and Women's Hospital). RHP uses TruSeq Custom Amplicon Kit (Illumina, Inc. San Diego, Calif., USA) and contains 95 genes (50 for AML/MDS, 8 for MPN, 27 for ALL, and 10 others). For oncogenes, known mutation hotspots are targeted; and for tumor suppressor genes the entire coding sequence is analyzed. The average amplicon size is 250-bp and about 50% of the regions are covered on both strands. Library preparation was according to manufacturer's instruction and sequencing was 150 bp paired-end reads with MiSeq v2.2 chemistry. Raw data was analyzed with Illumina on-board Real-Time-Analysis (RTA v.2.4.60.8) software and MiSeq Reporter. The VCF files were filtered with a cutoff for any nucleotide position with 10 or more variant reads or with 5-9 variant reads (if allele frequency >33%) as well as a Q score greater than 30 and germline single nucleotide polymorphisms were removed by comparison to dbSNP database (NCBI Human Build 141). The filtered variant lists were manually reviewed and BAM file examined in Integrated Genome Viewer (IGV, Broad Institute).

For 13 subjects from JHS, DNA obtained from a peripheral blood sample collected 4 to 8 years after the original DNA was available for analysis. RHP was used as described above to assess VAF of the previously detected mutations at the second time point, and to assess for the acquisition of new mutations.

Genes: ABL1, ASXL1, ATM, BCL11B, BCOR, BCORL1, BRAF, BRCC3, CALR, CBL, CBLB, CD79B, CEBPA, CNOT3, CREBBP, CRLF2, CSFIR, CSF3R, CTCF, CTNNB1, CUX1, CXCR4, DMNT3A, DNMT3B, EED, EGFR, EP300, ETV6, EZH2, FANCL, FBXW7, FLT3, GATA1, GATA2, GAT A3, GNAS, GNB1, IDH1, IDH2, IKZF1, IKZF2, IKZF3, IL7R, JAK1, JAK2, JAK3, KIT, KRAS, LUC7L2, MAP2K1, MEF2B, MPL, MYD88, NOTCH1, NOTCH2, NOTCH3, NPM1, NRAS, NT5C2, PAX5, PDGFRA, PDS5B, PHF6, PIGA, PIK3CA, PIM1, PRPF40B, PRPF8, PTEN, PTPN11, RAD21, RET, RIT1, RPL10, RUNX1, SETBP1, SETD2, SF1, SF3A1, SF3B1, SH2B3, SMC1A, SMC3, SRSF2, STAG2, STAT3, TET2, TLR2, TP53, U2AF1, U2AF2, WHSC1, WT1, XPO1, ZRSR2.

Statistics and analysis plan. All statistical analyses were performed using R. Cox proportional hazards and Kaplan-Meier analysis was performed using the survival package of the R software environment provided online by the R Foundation. Competing risks regression (CRR) was used to estimate hazard ratios for developing hematologic malignancy with death as the competing risk (Fine J P and Gray, R J. Journal of the American Statistical Association 1999; 94:496-509). CRR and cumulative incidence analysis was performed using the R cmprsk package. Fixed-effects meta-analysis using beta-coefficients for risk estimates from individual cohorts was used to provide summary hazard ratios across heterogeneous cohorts. Meta-analysis was performed using the R meta package.

Analyses for factors associated with clonal hematopoiesis were performed using logistic regression with the pre-determined variables age, sex, type 2 diabetes status, and ancestry.

Primary outcomes for clinical associations with clonal hematopoiesis were predetermined to be all-cause mortality and hematologic malignancy, using Cox proportional hazards models and CRR, respectively. Association of mutations with blood counts was also a primary analysis. For hematologic malignancy outcomes, only events incident to the time of DNA collection were considered. Red cell distribution width was also included as a variable in the survival analysis because of its association with mutations and previous reports of its association with increased mortality.

An analysis of cause specific mortality revealed an increase in cardiovascular deaths. For this reason, Applicants examined incident events of coronary heart disease and ischemic stroke using CRR and the pre-determined variables age, sex, type 2 diabetes status, body mass index, and systolic blood pressure. For some subjects, data was also available on smoking status, total cholesterol, and high-density lipoprotein. This was examined as a subgroup analysis in a separate regression (see Supplementary Tables S12-13).

For some analyses, a cutoff of VAF at 0.10 was used. This value was chosen because it was close to the median VAF in the dataset and is roughly the lower limit of detection using Sanger DNA sequencing, and was thus used to designate large versus small clone size.

Example 5: Supplemental Tables

Supplementary Table S1 Cohort descriptions and baseline characteristics T2D Clinical information No T2D T2D unknown available Cohort descriptions and sample ascertainment References TOTAL Overall (number with clone) 9303(367) 7861(378) 18(1) Female (number th clone) 4889(184) 3845(173) 8(0) Mean age (SD) 57(13) 59(10) Mean BMI (SD) 28(5.8) 29(5.6) Population-based JHS (Jackson Heart Study) Survial, malignacy, The Jackson Heart Study is a population-based cohort of 5,301 Wilson, J.G., et al. (2005). “Study design for genetic Overall (number with clone) 1801(57) 589(25) 18(1) blood counts, African-Americans living in the Jackson, Mississippi analysis in the Jackson Heart Study.” Ethnicfty and Female (number with clone) 1003(30) 401(18) 8(0) cardiovascular metropolitan area. Subjects were enrolled as random members of Disease 15:30-37; Taylor, HA. (2005). “The Mean age (SD) 5.1(13) 58(11) events community, volunteers, as part of the Aerosclerosis Risk in Jackson Head Study: An Overview.” Ethnicity and Mean BMI (SD) 31(6.8) 34(6.4) Communities (ARIC), or secondary family members. At 12- Disease 15:1-3; Fuqua, SR et al., (2005). rnonth intervals after the baseline clinic visit (Exam 1), “Recruiting African-American Research participants were contacted by telephone to: update Participation in the Jackson Heart Study. Methods, information; confirm vital statstics: document interim Response Rates, and Sample Description.” medical events, hospitalzations, and functional status; Ethnicity and Disease 15:18-29; Fuqua, S.R. et al., and obtain additional sociocultural information. Questions (2005). abut medical events, symptoms of cardiovascular disease and functional status were repeated annually. Ongoing cohort surveillance includes abstraction of medical records and death certificates for relevant International Classification of Diseases (ICD) codes and adjudication of nonfatal events and deaths. Clinical information No T2D T2D available Cohort descriptions and sample ascertainment References GoT2D Botnia (The Botnia Study), Survival, blood The Botnia Project was started in 1990 to study risk factors Flannick J., et al. (2013). “Assessing the phenotypic Diabetesieg (Scania Diabetes counts, for T2D in western Finland and southern Sweden and includes effects in the general population of rare variants in Registry), Helsinki-sib (Helsinki cardiovascular ~11,000 people from ~1400 families. The Scania Diabetes Registry genes for a dominant Mendelian form of diabetes.” siblings with diabetes cohort), events contains over 7000 diabetes patients recruited at hospitals in Scalia, Nat Genet 45(11): 1380-1385; Groop, L., et al. Malmõ-sib (siblings in Malmõ, Sweden as from 1996. The majority of the patents come from the (1999). “Metabolic consequences of a family Sweden) city of Malmõ, and they account for about 25% of all diabetic history of NIDDM (the Botnia study): evidence for Overall (number with clone) 224(12) 369(14) patients in the region. Death information was obtained sex-specific parental effects. “Diabetes 45(11): Female (number with clone) 108(6) 211(5) from the Finnish or Swedish Nath and Hospital Discharge 1585-1593; Lindholm, E., at al. (2001). “Classifying Mean age (SD) 65(10) 57(10) Registers. Individuals were ranked according to a liability diabetes according to the new WHO clinical Mean BMI (SD) 30(3.7) 25(2.7) model that measured risk for T2D. Briefly, liability scores stages.” Eur J Epidemiol 17(11): 983-989. were computed as the difference between diabetes status and the predicted risk based on age, BMI and gender; extreme cases were selected to have the highest liability scores (with diabetes but with low predicted risk for diabetes), and extreme controls were selected to have the lowest liability scores (without diabetes but with high predicted risk for diabetes). FUSION (Finland-United States Survival, blood The Finland-United States Investigation of NIDDM Genetics Valle, T., et al. (1998). “Mapping genes for NIDDM. Investigation of NIDDM counts, (FUSION) study is a long-term effort to identify genetic Design of the Finland-United States Investigation of Genetics Study) cardiovascular variants that predispose to type 2 diabetes (T2D) or that NIDDM Genetics (FUSION) Study. “Diabetes Care Overall (number with clone) 414(21) 470(21) events impact the variability of T2D-related quantitative traits. 21(6): 949-958; Scott, Let al. A Female (number with clone) 213(11) 201(10) Unrelated T2D cases were selected from FUSION affected- genome-wide association study of type 2 diabetes Mean age (SD) 63(7.2) 58(8.0) sibpair families and from stage 2 replication. NGT controls with in Finns detects multiple Mean BMI (SD) 28(3.9) 31(5.5) higher age and BMI were prioritized and were frequency susceptibility variants. Science 316(5829), 1341- matched to cases by birth province. 1345 (2007) KORA KORA is a regional research platform for population-based Wichmann, H. E. et al. (2005). “KORA-gen- Overall (number with clone) 91(11) 97(6) studies, subsequent follow-up studies and family studies, resource for population genetics, controls and a Female (number with clone) 58(9) 43(3) established in 1996. Cases and controls were all of German broad spectrum of disease phenotypes. Mean age (SD) 70(5.6) 61(8.1) ancestry. Cases were identified by self report of ILD in a Gesundheitswesen 67 Suppl 1: S26-30. Mean BMI (SD) 35(3.5) 28(2.8) personal interview which was valilated by a questionnaire mailed to the treating physician and/on by medical chart review. Controls were non-diabetic as defined by self-report. MPP (Malmo Preventive Project) The MPP was started in the early 1970's as a screening Overall (number with clone) 222(14) 110(8) survey in the middle-aged population of Malmõ, the Berglund, G., et al. (2000). “Long-term outcome of Female (number with clone) 87(1) 51(5) third largest city of Sweden. Subjects barn in Malmõ and the Malmo preventive project mortality and Mean age (SD) 68(5.2) 47(6.1) residents of the city were invited for a clinical examination, cardiovascular morbidity.” J Intern Mc-d 247(1): 1929 Mean BMI (SD) 36(1.8) 23(1.4) questionnaire and blood sampling. In all 224,44 men and 10,902 women participated during the period 1974-1992 Cases and controls were selected as for Botnia. STT (UKT2D Consortium, Controls) Non-diabetic controls selected from the Twins UK Study. Moayyeri, A., et al. (2013). “The UK Adult Twin Overall (number with clone) 319(14) * A twin pair was considered for selection if there was no Registry (TwinsUK Resource).” Twin Res Hum Female (number with clone) 265(11) * recorded family history of diabetes, neither twin was ever Genet 16(1): 144-149. Mean age (SD) 61(10) * recorded as impaired glucose tolerant, there were available Mean BMI (SD) 31(5.9) * quantative trait and genetic data and no evidence of admixture. From set of qualifying twin pairs, the best control twin was selected from each pair with the lowest ratio of fasting glucose level to BMI across all readings. SIGMA MEC (Multiethnic cohort, The MEC consists of 215,251 men and women in Hawaii Kolonel LN, Henderson BE, Hankin JH, Nomura AM, Hispanics in Los Angeles) and Los Angeles. Those self-idented as Latinos were used Wilkens LR, et al. (2(100)A multiethnic cohort in Overall (number with clone) 445(24) 496(27) Survival, for sequencing in a prior study. Between 1993 and 1996, Hawaii and Los Angeles: baseline characteristics. Female (number with clone) 228(13) 263(14) malignancy, adults between 45 and 75 years old were enrolled. Am J Epidemiol 151:346-357. Mean age (SD) 68(7.2) 68(7.3) cardiovascular Potential cohort members were identified through Mean BMI (SD) 27(4.3) 30(5.7) events Department of Motor Vehicles divers' license files, voter registration files and Health Care Financing Administration data files. Between 1995 and 2004, blood specimens were collected from ~67,000 MEC partidpants. Controls were frequency matched to cases on sex, ethnicity and age at entry into the cohort (5-year age groups) and place of birth (U.S. vs. Mexico, South or Central America). Persons in the cohort who develop carer are identified through Surveillance, Epidemiology, and End Results (SEER) Program registies that have been established by state statute in Hawaii and California. MexB1 (UNAM/INC MNSZ Cases were recruited at the outpatient diabetes clinic of the SIGMA 12D Consortium, et al. (2014). “Sequence Diabetes Study) Department of Endocrinology and Metabolism of the variants in SLC16A11 are a CORM risk factor for Overall (number with clone) 543(7) 550(20) Institute Nacional de Ciencias Medicas y Nutrición Salvador type 2 diabetes in Mexico.” Nabre 506(7486): 97-101. Female (number with clone) 336(6) 326(13) Zubirán (INCMNSZ). All Mexican-mestizo individuals were Mean age (SD) 55(9.4) 55(13) invited to participate in the study. Control subjects were recruited Mean BMI (SD) 28(3.8) 28(4.4) from a cohort of adults aged 45 years or older among government employees, blue collar workers and subjects seeking for attention in medical units for any condition besides those considered as exclusion criteria (diabetes, coronary heart disease, stroke, transient ischemic attack,lower limb amputations, alcoholism (more than 10 servings of alcohol per week) or any disease that in opinion of the researcher may limit life expectancy to less than 2 years). Diagnosis of type 2 diabetes was done following the MexB2 (Diabetes in Mexico American Diabetes Association (ADA) criteria Study) Overall (number with clone) 177(3) 393(11) Individuals were recruited from two tertiary SIGMA T2D Consortium, stat, (2014). “Sequence Female (number with clone) 134(3) 275(9) level institutions (IMSS and ISSSTE) boated in variants in SLC16A11 are a COMMOR risk factor Mean age (SD) 56(9.9) 57(12) Mexico Cty. Unrelated healthy sulipcts older for type 2 diabetes in Mexico.” Mean BMI (SD) 28(4.6) 29(5.5) than 45 years and with fasting glucose levels Nature 506(7486): 97-101. below 100 mgicIL were clasfiled as controls. Unrelated individuals, older than 18 years, with either previous T2D diagnosis or fasting glucose levels above 125 mg/dL were included as T2D cases. MexB3 (Mexico City Diabetes The Mexico City Diabetes Study is a population based SIGMA T2D Consortium, et al. (2014). “Sequence Study) prospective investigation. All 35-64 years of age men variants in SLC16A11 are a common risk factor for Overall (number with clone) 550(23) 281(9) and non-pregnant women residing in the study site type 2 diabetes in Mexico.” . Female (number with clone) 334(14) 164(3) (low income neighborhoods equivalent to 6 census Nature 506(7486): 97-101 Mean age (SD) 62(7.6) 64(7.5) tracks with a total population of 15,000 inhabitants) were Mean BMI (SD) 29(4.8) 30(5.5) interviewed and invited to participate in the study. There was a response rate of 67% for the initial exam. Diagnostic criteria for type 2 diabetes were recommended by the ADA. T2D-GENES AW (Wake Forest School of Medicine Study) Cases are self-reported diabetics with diabetic nephro- Palmer, N. D. et al. A genorne-wide association Overall (number with clone) 531(18) 529(48) pathy, recruited from dialysis clinics with age of onset ≥ search for type 2 diabetes genes in African Female (number with clone) 303(9) 317(28) 25. Controls were recruited from community and internal Americans. PLoS One 7:e29202. (2012) Mean age (SD) 51(12) 64(9.1) medicine clinics and had a current diagnosis of diabetes Mean BMI (SD) 30(7.0) 29(6.6) or renal disease. EK (Korea Association Research Project) Cases selected for age of onset of T2D ≥40 years. Cho, Y. S. at al. A large-scale genome-wide Overall (number with clone) 554(33) 521(10) Participants with early onset T2D and family association study of Asian populations uncovers Female (number with clone) 324(18) 237(4) history were prioritized. Controls were selected for genetic factors influendng eight quantitative traits. Mean age (SD) 63(3.6) 54(7.5) not having current or past diabetes and no diabetic Nat. Genet. 41, 527-534 (2009) Mean BMI (SD) 24(3.1) 26(3.3) medications. Older subjects with normal glucose were prioritzed. Study and Singapore Cases were clinically ascertained T2D from primary care Sim, X. et al. Transferability of type 2 diabetes Prospective Study Program clinics. Individuals with early age of diagnosis and with implicated loci in multi-ethnic cohorts from Overall (number with clone) 592(25) 476(24) at least one first degree relative with T2D were preferentially Southeast Asia. PLoS Genet 7(4), e1001363 (2011) Female (number with clone) 363(11) 250(11) selected. Controls were defined as: fasting bbod glucose < 6 Mean age (SD) 58(7.0) 58(9.3) mmoll, no personal history of diabetes, and no anti- Mean BMI (SD) 23(3.4) 26(3.8) diabetic medication. Older cotois preferentially selected. HA ( Hispanics from San Cases were drawn from four separate family studies and Mitchell, B. D. at al. Genetic and environmental Antonio Family Heart Study, met the following criteria: ADA 2002 criterion, WHO contributions to cardiovascular risk factors in San Antonio Family Diabetes/ 1999 criteria, or physician reported diangosis with Mexican Americans. The San Antonio Family Gallbladder Study, Veterans current medical therapy. Controls defined by not having Heart Study. Circulation 94, 2159-2170 (1996); Administration Genetic fasting glucose <126 mg at each visit and no history of Hunt, K. J. at al. Genome-wide linkage analyses Epidemiology Study, and the prior diabetic medication. of type 2 diabetes in Mexican Americans: the Investigation of Nephropathy San Antonio Family Diabetes/Gallbladder Study. and Diabetes Study family Diabetes 54, 2655-2662 (2005); Coletta, D.K. component) at al. Genome-wide linkage scan for genes Overall (number with clone) 111(6) 142(3) influencing plasma triglycerde levels in the Veterans Female (number with clone) 62(4) 90(1) Administration Genetic Epidemiology Study. Mean age (SD) 44(14) 51(12) Diabetes 58, 279-284 (2009); Knowler, W. C. Mean BMI (SD) 30(5.5) 33(6.3) et al. The Family Investigation of Nephropathy and Diabetes (FIND): design and methods. J. Diabetes Complicat 19, 1-9(2005) HS (Hispanics in Starr County, Texas) Cases defined by fasting glucose ≥ 140 mg/dl on more than Hanis, C. L at al. Diabetes among Mexican Overall (number with clone) 704(3) 751(32) 1 occasion or self-reported physician-diagnosed diabetes with Americans in Starr County, Texas. Am, J. Epidemiol. Female (number with clone) 506(3) 449(15) current medical therapy. In instances where cases were 118, 659-672 (1983); Below JE, et al. Gnome- Mean age (SD) 39(9.9) 56(12) drawn from families, the individual with youngest wide association and meta-analysis in populations Mean BMI (SD) 30(6.2) 32(6.4) age at onset was chosen. Controls ascertained from from Starr County, Texas and Mexico City identify epidemiologically represented sample of individuals in Starr type 2 diabetes susceptibilty loci and enrichment County, TX with individuals with known diagnosis for eQTLs in top signals. Diabetologia 54, 2047- of diabetes excluded. Controls are signficantly 2055(2011) younger on average. SL (London Life Sciences A population-based cohort study of Indian Asians living in Chambers, J.C. et al. Genome-wide association Population Study [UK Indian Asians]) West London, UK wth all 4 grandparents born on the Indian study identifies variants in TMPRSS6 associated Overall (number with clone) 538(24) 531(16) subcontinent Prevalent T2D defined as previous physician with hemoglobin levels. Nat. Genet. 41, 1170-1172 Female (number with clone) 85(4) 75(2) diagnosis of diabetes on treatment, with onset of diabetes (2009); Chambers, J.C. et al. Common genetic Mean age (SD) 63(9.2) 53(5.6) after The age of 18 years and without insulin use in the variation near melatonin receptor MTNR1B Mean BMI (SD) 27(3.5) 27(2.9) first year after diagnosis; or fasting plasma glucose > contributes to raised plasma glucose and increased 7.0 mmol/L. Controls defined as no previous history of risk of type 2 diabetes among Indian Asians and diabetes, no anti-diabetic medication, and fasting plasma European Caucasians. Diabetes 58, 2703-2708 glucose <6.0 mmol/L. (2009); van der Harst, P. et al. Seventy-five genetic loci influencing the human red blood cell. Nature 492, 369-375 (2012) SS (Singapore Indian Eye Study Cases selected for HbA1c > 6.5% or personal history of diabetes Sim, X. at al. Transferability of type 2 diabetes [Singapore Indians]) with age at diagnosis available. Preferentialy selected cases with implicated loci in multi-ethnic cohorts from Overall (number with clone) 585(15) 563(31) at least one first degree relative with T2D. Controls selected Southeast Asia. PLoS Genet. 7(4), e1001363 (2011) Mean age (SD) 56(10) 61(9.7) for HbAlc <6%, no personal history of diabetes, and not Female (number with clone) 288(5) 250(14) taking antidiabetes medication. Older controls Mean BMI (SD) 25(4.8) 27(5.1) preferentially selected. UA (Ashkenazis) Survival, blood Subjects in this cohort are of Ashkenazi Jewish origin, defined as Altzmon, G. et al Lipoprotein genotype and Overall (number with clone) 342(42) 506(49) counts haring all four grandparents born in Northern or Eastern Europe; conserved pathway for exceptional longevity in Female (number with clone) 195(20) 242(18) subjects with known or supected Sephardic Jewish or non-Jewish humans. PLoS Biol. 4(4), e113 (2006); Altzmon, G. Mean age (SD) 79(13) 66(8.6) ancestry excluded. T2D cases were selected from two separate et al Evolution in health and rnedicine Sackler Mean BMI (SD) 25(4.3) 27(3.2) DNA collections: 1. Genome-wide, affected-sibling-pair linkage colloquium: Genetic variation in human telomerase study (Permutt et al. Diabetes 2001) or 2. Study to determine is associated with telomere length in Ashkenazi genetic risk for diabetic complications (Blech et al. PLoS One centenarians. Proc Nati Acad Sci USA. 107 (Suppl1), 2011). Controls were selected for fasting blood glucose < 7 1710- 1717(2010); Permutt, M.A. et al. A mmol/l, no personal history of diabetes, and no anti-diabetic genome scan for type 2 diabetes suseptibilty loci medications. Controls included elderly ( > 90 years) individuals in a genetically isolated population. Diabetes 50(3), who were part of the Longevity Genes Project. 681-685 (2001); Blech et al. Predicting diabetic nephropathry using a multifactorial genetic model. PLoS One 6(4), e18743 (2011) UM (Metabolic Syndrome in Men Study) The METSIM Study includes 10,197 men, aged from 45 to 73 Stancakova, A. et al. Changes in insulin sensitivity Overall (number with clone) 500(9) 487(24) years, randomly selected from the population register of the and insulin release in relation to glycemia and Female (number with clone) * * town of Kuopio, Eastern Finland, and examined in 2005-2010. glucose tolerance in 6,414 Finnish men. Diabetes Mean age (SD) 55(4.6) 60(6.7) The aim of the study is to investigate genetic and non-genetic 58, 1212-1221 (2009) Mean BMI (SD) 26(3.2) 31(5.1) factors associated with the risk of type 2 diabetes (T2D), cardiovascular disease (CVD), and insulin resistance-related trans in a cross-sectional and longitudinal setting. Unrelated T2D cases with family history of diabetes were selected. Unrelated NGT controls were selected, prioritizing older individuals with no family history of diabetes.

SUPPLEMENTARY TABLE 2 List of hematopoietic genes and variants queried Number of Gene variants name Reported mutations used for variant calling Accession found ARID1A Frameshift/nonsense, A305V, M872T NM_006015 0 ASXL1 Frameshift/nonsense in exon 11-12 NM_015338 62 BCL10 Frameshift/nonsense/splice-site NM_003921 0 BCL11B Frameshift/nonsense/splice-site, A360T, C432Y, H445Y, R447H, G452K, H479Y, G596S, NM_138576 1 L617Q, G847R BCL6 R40H, Y111H, P341H, S350R, R585W, Q679K NM_001130845 0 BCOR Frameshift/nonsense/splice-site NM_001123385 4 BCORL1 Frameshift/nonsense/splice-site NM_021946 2 BIRC3 Frameshift/nonsense/splice-site exon 2 NM_182962 1 BRAF G464E, G464V, G466E, G466V, G469R, G469E, G469A, G469V, V471F, V472S, N581S, I582M, NM_004333 2 I592M, I592V, D594N, D594S, D594V, D594E, F595L, F595S, G596R, L597V, L597S, L597Q, L597R, A598V, V600M, V600L, V600K, V600R, V600E, V600A, V600G, V600D, K601E, K601N, R603*, W604R, W604G, S605S, S605F, S605N, G606E, G606A, G606V, H608R, H608L, G615R, S616P, S616F, L618S, L618W BRCC3 Frameshift/nonsense/splice-site NM_024332 6 BTG1 M1I, H2Y, P3R, Y5H, M11I, S23A, F25C, R27H, K29Q, L31F, Q36H, L37M, Q38E, F40C, E46D, NM_001731 0 E46Q, A49P, P58L, E59D, L94V, I115V, E117D, N165S BTG2 A45T, A45E NM_006763 0 CARD11 E93D, G123S, G126D, T128M, F130I, R179W, K182N, M183L, K215M, D230N, L232LI, NM_032415 1 M240MGLNKM, K244T, S250P, S250P, L251P, L251P, V266*, D338G, T353*, D357V, Y361H, M365K, D387V, D387V, D401V, R418S, R423W, E432K, E626K CBL RING finger missense p.381-421 NM_005188 12 CBLB RING finger missense p.372-412 NM_170662 0 CCND3 Frameshift/nonsense/splice-site, T211A, P212L, V215G NM_001760 0 CD58 Frameshift/nonsense/splice-site, G210C, G210S NM_001779 1 CD70 L60R, G66R, F186S NM_001252 0 CD79A del191-226, del179-226, del191-208 NM_001783 0 CD79B V9A, D89G, Y92F, D193G, D194G, Y196C, Y197C, Y197D, Y197H, T207fs, Y208*, V212A, NM_000626 1 del195_197, del196_229, del193_229, del205_229 CDKN2A Frameshift/nonsense/splice-site NM_000077 0 CDKN2B Frameshift/nonsense/splice-site NM_004936 0 CEBPA Frameshift/nonsense/splice-site NM_004364 0 CHD2 H620L, F1146L, L1270F NM_001271 0 CNOT3 Frameshift/nonsense, E20K, R57W, R57Q, E70K NM_014516 1 CREBBP Frameshift/nonsense/splice-site, D1435E, R1446L, R1446H, R1446C, Y1450C, P1476R, NM_004380 6 Y1482H, H1487Y, W1502C, Y1503D, Y1503H, Y1503F, S1680del CRLF2 F232C NM_022148 0 CSF1R L301F, L301S, Y969C, Y969N, Y969F, Y969H, Y969D NM_005211 0 CSF3R T615A, T618I, truncating c.741-791 NM_000760 0 CTCF Frameshift/nonsense, R377C, R377H, P378A, P378L NM_006565 0 CUX1 Frameshift/nonsense NM_181552 3 DDX3X R276K, R376S, R376C, D506Y, R528C, R528H, R534S, R534H, P568L NM_001356 2 DIS3 R780K, R780T, R780H, R780S, D488H, P480Q, D488N, S477R, R467Q, M662R, R689Q, R514K, NM_014953 0 R382Q DNMT3A Frameshift/nonsense/splice-site, P307S, P307R, R326H, R326L, R326C, R326S, R366P, R366H, NM_022552 403 R366G, A368T, F414L, F414S, F414C, C497Y, Q527H, Q527P, Y533C, G543A, G543S, G543C, L547H, L547P, L547F, M548I, M548K, G550R, W581R, W581S, W581C, G646V, G646E, L653W, L653F, V657A, V657M, R659H, Y660C, R676W, R676Q, G685R, G685E, G685A, D686Y, D686G, G699R, G699S, G699D, P700S, P700R, P700Q, D702N, D702Y, V704M, V704G, I705F, I705T, I705S, C710S, S714C, N717S, N717I, P718L, R720H, R720G, Y724C, R729Q, R729W, R729G, F731L, F732del, F732S, F732L, F734L, F734C, Y735C, Y735N, Y735S, R736H, R736C, R736P, L737H, L737V, L737F, L737R, A741V, R749C, R749L, F751L, F752del, F752C, F752L, F752I, F752V, L754R, L754H, F755S, F755I, F755L, M761I, M761V, G762C, S770W, S770P, R771Q, F772I, F772V, L773R, E774K, E774D, D781G, R792H, G796D, G796V, N797Y, N797H, P799R, P799H, R803S, P804S, P804L, S828N, K829R, Q842E, P849L, D857N, W860R, F868S, G869S, G869V, M880V, S881R, S881I, R882H, R882P, R882C, R882S, Q886R, G890D, L901R, L901H, P904L, F909C, A910P EBF1 Frameshift/nonsense, M1R, S7G, F54S, G143V, G171D, Q196P, N237K, S238T, S238Y, R381S NM_024007 0 EED Frameshift/nonsense/splice-site, L240Q, I363M NM_003797 0 EP300 Frameshift/nonsense/splice_site, VF1148_1149del, D1399N, D1399Y, P1452L, Y1467N, NM_001429 1 Y1467H, Y1467C, R1627W, A1629V ETV6 Frameshift/nonsense/splice-site NM_001987 1 EZH2 Frameshift/nonsense/splice-site, Q62R, N102S, F145S, F145C, F145Y, F145L, G159R, E164D, NM_001203247 2 R202Q, K238E, E244K, R283Q, H292R, P488S, R497Q, R561H, T568I, K629E, Y641N, Y641H, Y641S, Y641C, Y641F, D659Y, D659G, V674M, A677G, A677V, R679C, R679H, R685C, R685H, A687V, N688I, N688K, H689Y, S690P, I708V, I708T, I708M, E720K, E740K EZR I245M NM_003379 0 FAM46C Frameshift/nonsense/splice-site NM_017709 1 FAS Frameshift/nonsense/splice-site NM_000043 0 FBXO11 Frameshift/nonsense/splice-site NM_001190274 0 FBXW7 Frameshift/nonsense/splice-site, E74A, D101V, F280L, R465H, R505C, G597E, R1165Q NM_033632 1 FLT3 V579A, V592A, V592I, F594L, M737I, FY590-591GD, D835Y, D835H, D835E, del835 NM_004119 1 FOXP1 Frameshift/nonsense/splice-site NM_032682 1 FYN L174R, R176C, Y531H NM_002037 0 GATA1 Frameshift/nonsense/splice-site NM_002049 0 GATA2 Frameshift/nonsense/splice-site, R293Q, N317H, A318T, A318V, A318G, G320D, L321P, L321F, NM_001145661 0 L321V, Q328P, R330Q, R361L, L359V, A372T, R384G, R384K GATA3 Frameshift/nonsense/splice-site ZNF domain, R276W, R276Q, N286T, L348V, NM_001002295 0 GNA13 I34T, G57S, S62F, M68K, Q134R, Y145F, L152F, E167D, Q169H, R264H, E273K, V322G, V362G, NM_006572 0 L371F GNAS R201(844)S, R201(844)C, R201(844)H, R201(844)L, Q227(870)K, Q227(870)R, Q227(870)L, NM_016592 8 Q227(870)H, R374(1017)C GNB1 K57N, K57M, K57E, K57T, I80T, I80N NM_002074 22 HIST1H1B S89N, S89R, G101D, G73A, K84N, A123D NM_005322 0 HIST1H1C P118S, P129A, K156R, K187R, K/G81/83N/A NM_005319 1 HIST1H1D Frameshift/nonsense NM_005320 0 HIST1H1E A158T, A167V, P196S, K202E, K205R NM_005321 0 HIST1H3B A48S, S87N, S87T NM_003537 0 HLA-A Frameshift/nonsense, G124D, A164T NM_002116 0 ID3 Frameshift/nonsense/splice-site, S39R, V55Q, P56S, L64F, S65R, I74V, L80R, H96R, NM_002167 0 IDH1 R132C, R132G, R132H, R132L, R132P, R132V, V178I NM_005896 0 IDH2 R140W, R140Q, R140L, R140S, R172W, R172G, R172K, R172T, R172M, R172N, R172S NM_002168 3 IKBKB K171E NM_001556 0 IKZF1 Frameshift/nonsense NM_006060 1 IKZF2 Frameshift/nonsense NM_016260 0 IKZF3 Frameshift/nonsense NM_012481 0 IL7R exon 6 cysteine insertion NM_002185 0 INTS12 M445I NM_020395 0 IRF4 N2S, S18T, I32V, L40V, Q60K, Q60H NM_002460 0 IRF8 Frameshift/nonsense from c.377-426, S34T, S55A, T80A, K108E, NM_002163 0 JAK1 T478A, T478S, V623A, A634D, L653F, R724H, R724Q, T782M, L783F NM_002227 0 JAK2 N533D, N533Y, N533S, H538R, K539E, K539L, I540T, I540V, V617F, R683S, R683G, NM_004972 31 del/ins537-539L, del/ins538-539L, del/ins540-543MK, del/ins540-544MK, del/ins541-543K, del542-543, del543-544, ins11546-547 JAK3 M511T, M511I, A572V, A572T, A573V, R657Q, V715I, V715A NM_000215 1 JARID2 Frameshift/nonsense/splice-site NM_004973 1 KDM6A Frameshift/nonsense/splice-site, del419 NM_021140 2 KIT ins503, V559A, V559D, V559G, V559I, V560D, V560A, V560G, V560E, del560, E561K, NM_000222 1 del579, P627L, P627T, R634W, K642E, K642Q, V654A, V654E, H697Y, H697D, E761D, K807R, D816H, D816Y, D816F, D816I, D816V, D816H, del551-559 KLHL6 F49L, L58P, T64S, T64I, L65V, L65P, Q81, L90V, R98T, R98W NM_130446 1 KRAS G12D, G12A, G12E, G12V, G13D, G13C, G13Y, G13F, G13R, G13A, G13V, G13E, T58I, NM_033360 3 G60D, G60A, G60V, Q61K, Q61E, Q61P, Q61R, Q61L, Q61H, K117E, K117N, A146T, A146P, A146V LEF1 Frameshift/nonsense NM_016269 0 LRRK2 E155K, I543S NM_198578 0 LTB Frameshift/nonsense NM_002341 0 LUC7L2 Frameshift/nonsense/splice-site NM_016019 3 MALT1 V381F, Y750S NM_006785 0 MAP2K1 F53L, Q56P, K57T, K57N, K57E, I103N, C121S, N122D, P124Q NM_002755 0 MAP3K14 H683Q NM_003954 0 MED12 E33K, L36P, G44S, A59P, R521H, L1224F NM_005120 0 MEF2B Frameshift/nonsense/splice-site, I8F, L67R, Y69C, Y69H, E77K, N81K, N81Y, D83V, NM_001145785 0 D83A MLL Frameshift/nonsense NM_005933 0 MLL2 Frameshift/nonsense NM_003482 4 MPL S505G, S505N, S505C, L510P, del513, W515A, W515R, W515K, W515S, W515L, A519T, NM_005373 2 A519V, Y591D, W515-518KT MXRA5 Frameshift/nonsense/splice-site NM_015419 0 MYD88 V217F, S219C, M240T, S251N, P266, L273P NM_001172567 2 NOTCH1 Frameshift/nonsense NM_017617 2 NOTCH2 Frameshift/nonsense NM_024408 2 NPM1 Frameshift p.W288fs (insertion at c.859_860, 860_861, 862_863, 863_864) NM_002520 0 NRAS G12S, G12R, G12C, G12N, G12P, G12Y, G12D, G12A, G12V, G12E, G13S, G13R, G13C, NM_002524 6 G13N, G13P, G13Y, G13D, G13A, G13V, G13E, G60E, G60R, Q61R, Q61L, Q61K, Q61P, Q61H, Q61Q P2RY8 M52K, Y82C, R86C, C144Y, M189I, A251T, N254S, F255I, V256M NM_178129 0 PAPD5 Frameshift/nonsense NM_001040284 0 PAX5 Frameshift/nonsense/splice-site, S66N, P80R, NM_016734 0 PDS5B Frameshift/nonsense/splice-site, R1292Q NM_015032 0 PDSS2 Frameshift/nonsense NM_020381 1 PHF6 Frameshift/nonsense/splice-site, A40D, M125I, S246Y, F263L, R274Q, C297Y, H302Y, NM_001015877 1 H329L PIK3CA I543V, Q545G, G1007D, L1026P, M1040I, D1045N, H1047R NM_006218 1 POT1 Frameshift/nonsense/splice-site before OB domain (c.274), M1L, Y36N, K90E, Q94R, NM_015450 0 Y223C, H266L, G272V, C591W POU2AF1 P27A NM_006235 0 POU2F2 T223A, T223S, T239S, R282H, T307I, G392R NM_002698 0 PRDM1 Frameshift/nonsense/splice-site NM_001198 3 PRPF40B Frameshift/nonsense/splice-site, P15H, M58I, P405L, P562S, NM_001031698 1 PRPF8 M1307I NM_006445 0 PTEN Frameshift/nonsense/splice-site, D24G, R47G, F56V, L57W, H61R, K66N, Y68H, C71Y, NM_000314 0 F81C, Y88C, D92G, D92V, D92E, H93Y, H93D, H93Q, N94I, P95L, I101T, C105F, C105S, D107Y, L112V, H123Y, C124R, C124S, K125E, A126D, K128N, R130G, R130Q, R130L, G132D, I135V, I135K, C136R, C136F, K144Q, A151T, D153Y, D153N, Y155H, Y155C, R159K, R159S, R161K, R161I, G165R, G165E, S170N, S170I, R173C, Y174D, Y177C, H196Y, R234W, G251C, D252Y, F271S, D326G PTPN1 exon 1 frameshift/nonsense, Q9E NM_002827 0 PTPN11 G60V, G60R, G60A, D61Y, D61V, D61S, Y63C, E69K, E69G, E69D, E69Q, F71L, F71K, NM_002834 3 A72T, A72V, A72D, T73I, E76K, E76Q, E76M, E76A, E76g, E139G, E139D, N308D, N308T, N339S, P491L, S502P, S502A, S502L, S503V, G503S, G503A, G503E, Q506P, T507A, T507K RAD21 Frameshift/nonsense/splice-site, R65Q, H208R, Q474R NM_006265 6 RBBP4 E330K NM_005610 0 RHOA C16R, G17V, G17E, T19I, D120Y NM_001664 0 RIT1 Frameshift/nonsense, Q79, E81Q, E81G, F82L, F82C, F82I, F82V, M90I, R122L NM_006912 1 RPL10 R98S, Q123P NM_006013 0 RPL5 Frameshift/nonsense/splice-site, Q63R NM_000969 0 RPS15 G132S, A135V, A135G, S139A, K145N NM_001018 1 RPS2 R200G NM_002952 0 RUNX1 Frameshift/nonsense/splice-site, S73F, H78Q, H78L, R80C, R80P, R80H, L85Q, P86L, NM_001001890 0 P86H, S114L, D133Y, L134P, R135G, R135K, R135S, R139Q, R142S, A165V, R174Q, R177L, R177Q, A224T, D171G, D171V, D171N, R205W, R293Q SETBP1 D868N, D868T, S869N, G870S, I871T, D880N, D880Q NM_015559 0 SETD2 Frameshift/nonsense, V1190M NM_014159 4 SETDB1 Frameshift/nonsense, K715E NM_001145415 1 SF1 Frameshift/nonsense/splice-site, T454M, T474A, Y476C, A508G NM_004630 2 SF3A1 Frameshift/nonsense/splice-site, A57S, M117I, K166T, Y271C NM_005877 1 SF3B1 G347V, R387W, R387Q, E592K, E622D, Y623C, R625L, R625C, H662Q, H662D, K666N, NM_012433 27 K666T, K666E, K666R, K700E, V701F, A708T, G740R, G740E, A744P, D781G, E783K SFRS2 Y44H, P95H, P95L, P95T, P95R, P95A, P107H, P95fs NM_003016 11 SGK1 Frameshift/nonsense/splice-site NM_001143676 0 SMC1A K190T, R586W, M689V, R807H, R1090H, R1090C NM_006306 1 SMC3 Frameshift/nonsense, R155I, Q367E, D392V, K571R, R661P, G662C NM_005445 1 SOCS1 Frameshift/nonsense/splice-site, R48W NM_003745 0 SPRY4 I113N, G117R NM_001127496 0 STAG1 Frameshift/nonsense/splice-site, H1085Y NM_005862 1 STAG2 Frameshift/nonsense/splice-site NM_006603 1 STAT3 M206K, G618R, Y640F, N642H, N647I, D661N, D661H, D661Y, D661V NM_139276 4 STAT5A N642H NM_003152 0 STAT5B N642H, Y665F NM_012448 0 STAT6 N417Y, N417S, D419H, D419G, D419V, N421K, N430T, N430S NM_001178081 0 SUZ12 Frameshift/nonsense NM_015355 1 SWAP70 Frameshift/nonsense/splice-site NM_015055 0 TBL1XR1 Frameshift/nonsense/splice-site NM_024665 1 TCF3 N551K, V557E, V557G, D561E, D561V, D561N, M572K NM_003200 0 TET1 Frameshift/nonsense/splice-site, V128F, H1297Y, R1656C, V2120M, NM_030625 1 TET2 Frameshift/nonsense/splice-site, S282F, N312S, L346P, S460F, D666G, P941S, C1135Y NM_001127208 72 TMEM30A Frameshift/nonsense NM_018247 0 TNF L47F, H52Y, P58S NM_000594 0 TNFAIP3 Frameshift/nonsense, D117V, M476I, P574L NM_006290 3 TNFRSF14 Frameshift/nonsense/splice-site, Y26N, Y26D, C42P, C42W, A102P NM_003820 2 TP53 Frameshift/nonsense/splice-site, S46F, G105C, G105R, G105D, G108S, G108C, R110L, NM_001126112 33 R110C, T118A, T118R, T118I, K132Q, K132E, K132W, K132R, K132M, K132N, C135W, C135S, C135F, C135G, Q136K, Q136E, Q136P, Q136R, Q136L, Q136H, A138P, A138V, A138A, A138T, T140I, C141R, C141G, C141A, C141Y, C141S, C141F, C141W, V143M, V143A, V143E, L145Q, L145R, P151T, P151A, P151S, P151H, P152S, P152R, P152L, T155P, R158H, R158L, A159V, A159P, A159S, A159D, A161T, A161D, Y163N, Y163H, Y163D, Y163S, Y163C, K164E, K164M, K164N, K164P, H168Y, H168P, H168R, H168L, H168Q, M169I, M169T, M169V, T170M, E171K, E171Q, E171G, E171A, E171V, E171D, C176G, TRAF3 Frameshift/nonsense NM_145725 0 TYW1 R555Q, E601K NM_018264 0 U2AF1 D14G, S34F, S34Y, R35L, R156H, R156Q, Q157R, Q157P NM_006758 5 U2AF2 R18W, Q143L, M144I, L187V, Q190L NM_007279 0 UBR5 Frameshift/nonsense/splice-site exon 58 NM_015902 0 WT1 Frameshift/nonsense/splice-site NM_024426 0 XBP1 L167I, P326R NM_001079539 0 XPO1 E571A, E571K NM_003400 0 ZNF471 G483, P486, R423 NM_020813 0 ZRSR2 Frameshift/nonsense, R126P, E133G, C181F, H191Y, I202N, F239V, F239Y, N261Y, C280R, NM_005089 3 C302R, C326R, H330R, N382K Total 805

Supplementary Table S3 Called somatic variants in 160 hematopoeitic genes Variant Variant Reference Start Variant Reference Variant Variant Protein allele allele allele Gene name Chromosome position Type Allele Allele Classification Change cDNA change Accession fraction count count ID ASXL1 20 31021211 SNP C T Nonsense_Mutation p.R404* c.1210C > T NM_015338 0.302326 26 60 id16093 ASXL1 20 31021211 SNP C T Nonsense_Mutation p.R404* c.1210C > T NM_015338 0.106796 11 92 id5983 ASXL1 20 31021211 SNP C T Nonsense_Mutation p.R404* c.1210C > T NM_015338 0.063291 5 74 id3697 ASXL1 20 31021211 SNP C T Nonsense_Mutation p.R404* c.1210C > T NM_015338 0.194444 14 58 id6530 ASXL1 20 31021250 SNP C T Nonsense_Mutation p.R404* c.1210C > T NM_015338 0.052083 5 91 id12539 ASXL1 20 31021250 SNP C T Nonsense_Mutation p.R417* c.1250G > A NM_015338 0.04878 6 117 id6912 ASXL1 20 31021250 SNP C T Nonsense_Mutation p.R417* c.1250G > A NM_015338 0.052632 5 90 id17093 ASXL1 20 31021250 SNP C T Nonsense_Mutation p.R417* c.1250G > A NM_015338 0.241379 28 88 id7330 ASXL1 20 31021250 SNP C T Nonsense_Mutation p.R417* c.1250G > A NM_015338 0.177305 25 116 id4693 ASXL1 20 31021250 SNP C T Nonsense_Mutation p.R417* c.1250G > A NM_015338 0.069767 6 80 id10535 ASXL1 20 31021276 DEL C — Frame_Shift_Del p.Y425fs c.1275delC NM_015338 0.2 16 65 id17167 ASXL1 20 31021283 SNP C T Nonsense_Mutation p.Q428* c.1282C > T NM_015338 0.051948 4 73 id15889 ASXL1 20 31021295 SNP C T Nonsense_Mutation p.Q432* c.1294C > T NM_015338 0.232877 17 56 id12542 ASXL1 20 31021332 SNP C G Nonsense_Mutation p.S444* c.1331C > T NM_015338 0.041237 4 93 id9683 ASXL1 20 31021332 SNP C G Nonsense_Mutation p.S444* c.1331C > T NM_015338 0.047619 7 140 id3121 ASXL1 20 31021535 SNP C T Nonsense_Mutation p.Q512* c.1534C > T NM_015338 0.122449 24 172 id3907 ASXL1 20 31021538 SNP G T Nonsense_Mutation p.E513* c.1537G > T NM_015338 0.052632 7 126 id12605 ASXL1 20 31021543 DEL TG — Frame_Shift_Del p.T514fs c.1542_1543delTG NM_015338 0.24 58 179 id9964 ASXL1 20 31021543 DEL TG — Frame_Shift_Del p.T514fs c.1542_1543delTG NM_015338 0.09 20 201 id14785 ASXL1 20 31021550 SNP C T Nonsense_Mutation p.Q517* c.1549C > T NM_015338 0.067873 15 206 id15925 ASXL1 20 31021553 SNP G T Nonsense_Mutation p.E516* c.1552_1553delGA NM_015338 0.095238 12 114 id15929 ASXL1 20 31021553 DEL GA — Frame_Shift_Del p.E518fs c.1552_1553delGA NM_015338 0.16 41 220 id16839 ASXL1 20 31021586 SNP C T Nonsense_Mutation p.Q529* c.1585C > T NM_015338 0.179775 32 146 id15189 ASXL1 20 31021627 INS — GAAGATC Frame_Shift_Ins p.L542fs c.1626_1627ins NM_015338 0.13 14 91 id16126 GAAGATC ASXL1 20 31021637 SNP C T Nonsense_Mutation p.Q546* c.1636C > T NM_015338 0.194444 21 87 id15951 ASXL1 20 31021647 INS — TAACACT Frame_Shift_Ins p.R549fs c.1646_1647ins NM_015338 0.05 10 206 id2117 TAACACT ASXL1 20 31022238 SNP C T Nonsense_Mutation p.Q575* c.1723C > T NM_015338 0.166667 10 50 id2349 ASXL1 20 31022263 SNP G A Nonsense_Mutation p.W583* c.1748G > A NM_015338 0.08 6 69 id11045 ASXL1 20 31022286 INS — A Frame_Shift_Ins p.Y591fs c.1771_1772insA NM_015338 0.32 29 61 id5794 ASXL1 20 31022288 SNP C A Nonsense_Mutation p.Y591* c.1771_1772insA NM_015338 0.101695 18 159 id13763 ASXL1 20 31022297 SNP C A Nonsense_Mutation p.C594* c.1782C > A NM_015338 0.16129 10 52 id884 ASXL1 20 31022363 INS — T Frame_Shift_Ins p.D616fs c.1848_1849insT NM_015338 0.23 10 33 id1924 ASXL1 20 31022403 DEL CACCACTGC — Frame_Shift_Del p.H630fs c.1888_1910delCA NM_015338 0.18 8 37 id6015 CATAGAGAG CCACTGCCATAGAG GCGGC AGGCGGC ASXL1 20 31022416 DEL GAGAGGCG — Frame_Shiff_Del p.R634fs c.1901_1923delGA NM_015338 0.22 13 45 id4773 GCCACCACT GAGGCGGCCACCA GCCATC CTGCCATC ASXL1 20 31022441 INS — G Frame_Shift_Ins p.G642fs c.1926_1927insG NM_015340 0.33 6 12 id13857 ASXL1 20 31022441 INS — G Frame_Shift_Ins p.G642fs c.1926_1927insG NM_015338 0.24 6 19 id15227 ASXL1 20 31022441 INS — G Frame_Shift_Ins p.G642fs c.1926_1927insG NM_015339 0.38 6 10 id3122 ASXL1 20 31022441 INS — G Frame_Shift_Ins p.G642fs c.1926_1927insG NM_015341 0.38 8 13 id10828 ASXL1 20 31022441 INS — G Frame_Shift_Ins p.G642fs c.1926_1927insG NM_015342 0.31 11 24 id3059 ASXL1 20 31022589 SNP C T Nonsense_Mutation p.Q692* c.2074C > T NM_015338 0.067416 6 83 id12078 ASXL1 20 31202592 SNP C T Nonsense_Mutation p.R693* c.2077C > T NM_015338 0.108108 4 33 id12402 ASXL1 20 31022592 SNP C T Nonsense_Mutation p.R693* c.2077C > T NM_015338 0.166667 5 25 id5859 ASXL1 20 31022592 SNP C T Nonsense_Mutation p.R693* c.2077C > T NM_015338 0.156863 8 43 id4532 ASXL1 20 31022599 DEL A — Frame_Shift_Del p.Q695fs c.2084delA NM_015338 0.19 9 38 id2275 ASXL1 20 31022643 DEL G — Frame_Shift_Del p.G710fs c.2128delG NM_015338 0.34 11 21 id14955 ASXL1 20 31022757 SNP C T Nonsense_Mutation p.Q748* c.2242C > T NM_015338 0.3 9 21 id15381 ASXL1 20 3102784 SNP C T Nonsense_Mutation p.Q757* c.2269C > T NM_015338 0.279412 19 49 id9809 ASXL1 20 31022793 SNP C T Nonsense_Mutation p.Q760* c.2278C > T NM_015338 0.125 4 28 id12782 ASXL1 20 31022817 SNP C T Nonsense_Mutation p.Q768* c.2302C > T NM_015338 0.1 5 45 id2116 ASXL1 20 31022817 SNP C T Nonsense_Mutation p.Q768* c.2302C > T NM_015338 0.06 3 47 id11131 ASXL1 20 31022853 SNP C T Nonsense_Mutation p.Q780* c.2338C > T NM_015338 0.592308 19 46 id9635 ASXL1 20 31022922 SNP C T Nonsense_Mutation p.Q803* c.2407C > T NM_015338 0.112903 7 55 id10826 ASXL1 20 31022922 SNP C T Nonsense_Mutation p.Q803* c.2407C > T NM_015338 0.296296 16 38 id11861 ASXL1 20 31022982 DEL T — Frame_Shift_Del p.L823fs c.2467delT NM_015338 0.08 10 111 id2961 ASXL1 20 31022983 SNP T G Nonsense_Mutation p.L823* c.2467delT NM_015338 0.060403 9 140 id1808 ASXL1 20 31023271 INS — A Frame_Shift_Ins p.I919fs c.2756_2757insA NM_015338 0.22 18 64 id9984 ASXL1 20 31023354 SNP G T Nonsense_Mutation p.E947* p.2840A > G NM_015338 0.08 6 69 id1754 ASXL1 20 31023381 DEL CTTA — Frame_Shift_Del p.L956fs c.2866_2869delCTTA NM_015338 0.31 25 56 id14964 ASXL1 20 31023528 DEL T — Frame_Shift_Del p.F101fs c.3013delT NM_015338 0.1 11 96 id4445 ASXL1 20 31023554 DEL C — Frame_Shift_Del p.S1013fs c.3039delC NM_015338 0.12 11 81 id13638 ASXL1 20 31023625 SNP G A Nonsense_Mutation p.W1037* c.3110G > A NM_015338 0.072581 9 115 id16502 ASXL1 20 31023702 SNP C T Nonsense_Mutation p.Q1063* c.3187C > T NM_015338 0.052632 6 108 id3226 BCL11B 14 99641387 SNP C T Missense_Mutation p.G596S c.1786G > A NM_138576 0.404255 19 28 id3170 BCOR X 39922072 INS — G Frame_Shift_Ins p.H1367fs c.4099_4100insC NM _001123385 0.05 6 120 id1443 BCOR X 39930272 DEL CG — Frame_Shift_Del p.S1064fs c.3191_3192delCG NM_001123385 0.1 12 109 id5900 BCOR X 39933743 DEL T — Frame_Shift_Del p.S286fs c.856delA NM_001123385 0.43 12 16 id3439 BCOR X 39934170 INS — T Frame_Shift_Ins p.N143fs c.428_429insA NM_001123385 0.03 6 185 id6419 BCORL1 X 129147623 SNP C G Frame_Shift_Ins p.S292* c.875C > G NM_021946 0.162791 7 36 id16590 BCORL1 X 129159270 SNP C T Nonsense_Mutation p.R1332* c.3994C > T NM_021946 0.09375 6 58 id3227 BIRC3 11 102195867 INS — G Frame_Shift_Ins p.G209fs c.627_628insG NM_182962 0.04 7 163 id11974 BRAF 7 140453136 SNP A T Missense_Mutation p.V600E c.17991 > A NM_004333 0.196429 11 45 id9117 BRAF 7 140481402 SNP C T Missense_Mutation p.G469E c.1406G > A NM_004333 0.12973 24 161 id15992 BRCC3 X 154305490 SNP C T Nonsense_Mutation p.R81* c.2410 > T NM_024332 0.143791 22 131 id6955 BRCC3 X 154305490 SNP C T Nonsense_Mutation p.R81* c.241C > T NM_024332 0.084337 7 76 id10444 BRCC3 X 154305514 SNP C T Nonsense_Mutation p.R89* c.265C > T NM_024332 0.067416 6 83 id10536 BRCC3 X 154305514 SNP C T Nonsense_Mutation p.R89* c.265C > T NM_024332 0.116667 7 53 id14164 BRCC3 X 154301935 SNP G A Nonsense_Mutation p.W120* c.360C > A NM_024332 0.04 7 168 id8978 BRCC3 X 154306978 SNP G T Splice_Site c.e5 + 1 c403_splice NM_024332 0.104348 12 103 id8963 CARD11 7 2976745 SNP G A Missense_Mutation p.R423W c.1267C > T NM_032415 0.073171 3 38 id5921 CBL 11 119148921 SNP T C Missense_Mutation p.C381R c.11411 > C NM_005188 0.122807 7 50 id4445 CBL 11 119148922 SNP G A Missense_Mutation p.C381Y c.1142G > A NM_005188 0.34375 11 21 id15991 CBL 11 119148922 SNP G C Missense_Mutation p.C381S c.11420 > C NM_005188 0.071429 3 39 id5319 CBL 11 119148924 SNP A G Missense_Mutation p.K382E c.1144A > G NM_005188 0.064103 5 73 id3465 CBL 11 119148929 SNP A G Missense_Mutation p.I383M c.1149A > G NM_005188 0.06383 6 88 id12878 CBL 11 119148991 SNP G A Missense_Mutation p.C404Y c.1211G > A NM_005188 0.113333 17 133 id8673 CBL 11 119148991 SNP G A Missense_Mutation p.C404Y c.1211G > A NM_005188 0.072917 7 89 id15632 CBL 11 119148991 SNP G A Missense_Mutation p.C404Y c.1211G > A NM_005188 0.101562 13 115 id10499 CBL 11 119149235 SNP G A Missense_Mutation p.G415S c.1243G > A NM_005188 0.046875 6 122 id16015 CBL 11 119149235 SNP G A Missense_Mutation p.G415S c.1243G > A NM_005188 0.056 7 118 id15505 CBL 11 119149244 SNP T A Missense_Mutation p.F418I c.1252T > A NM_005188 0.082278 13 145 id3807 CBL 11 119149251 SNP G A Missense_Mutation p.R420Q c.1259G > A NM_005188 0.052632 5 108 id10937 CBL 11 117078587 SNP C T Missense_Mutation p.G210S c.628G > A NM_001779 0.368 46 79 id15637 CD79B 17 62006798 SNP T G Missense_Mutation p.Y196S c.587A > C NM_000626 0.086957 8 84 id6059 CNOT3 19 54646887 SNP G A Missense_Mutation p.E20K c.58G > A NM_014516 0.055556 12 204 id1662 CREBBP 16 3786067 DEL T — Frame_Shift_Del p.E1566fs c.4698delA NM_004380 0.03 7 236 id1443 CREBBP 16 3817721 DEL T — Frame_Shift_Del p.I1084fs c.3250delA NM_004380 0.05 8 149 id8258 CREBBP 16 3828056 INS — C Frame_Shift_Ins p.A690fs c.2068_2069insG NM_004380 0.03 6 182 id10673 CREBBP 16 3828080 INS — G Frame_Shift_Ins p.Q682fs c.2044_2045insC NM_004380 0.07 12 166 id3674 CREBBP 16 3843447 SNP G A Nonsense_Mutation p.R386* c.1156C > T NM_004380 0.054054 6 105 id14548 CREBBP 16 3860722 INS — G Frame_Shift_Ins p.Q286fs c.856_857insC NM_004380 0.08 10 122 id6570 CUX1 7 101559473 SNP C T Nonsense_Mutation p.Q37* c.1124A > T NM_181552 0.125 17 119 id3807 CUX1 7 101747648 SNP C T Nonsense_Mutation p.R147* c.443013 > A NM_181552 0.113636 15 117 id12656 CUX1 7 101837121 SNP G A Splice_Site c.e13 − 1 c.1077_splice NM 181552 0.121212 4 29 id2276 DDX3X X 41196685 INS — C Frame_Shift_Ins p.S24fs c.70_71insC NM_001356 0.09 18 190 id17182 DDX3X X 41205659 DEL CAGCAGTAT — Splice_Site c.e13 + 1 c.1497_splice NM_001356 0.17 20 97 id6943 GTAT DNMT3A 2 25457160 DEL A — Frame Shift_Del p.F909fs c.2727delT NM_022552 0.04 7 183 id8553 DNMT3A 2 25457160 DEL A — Frame Shift_Del p.F909fs c.2727delT NM_022552 0.05 7 142 id15476 DNMT3A 2 25457176 SNP G A Missense_Mutation p.P904L c.2711C > T NM_022552 0.164706 14 71 id3391 DNMT3A 2 25457176 SNP G A Missense_Mutation p.P904L c.2711C > T NM_022552 0.057143 4 66 id12744 DNMT3A 2 25457176 SNP G A Missense_Mutation p.P904L c.2711C > T NM_022552 0.054054 4 70 id8866 DNMT3A 2 25457176 SNP G A Missense_Mutation p.P904L c.2711C > T NM_022552 0.093333 7 68 id14640 DNMT3A 2 25457176 SNP G A Missense_Mutation p.P904L c.2711C > T NM_022552 0.068493 6 68 id15401 DNMT3A 2 25457176 SNP G A Missense_Mutation p.P904L c.2711C > T NM_022552 0.179368 15 61 id13118 DNMT3A 2 25457179 DEL GC — Frame_Shift_Dei p.A903fs c.2707_2708delGC NM_022552 0.06 6 88 id1518 DNMT3A 2 25457181 INS — A Frame_Shift_Ins p.F902fs c.2705_2706insT NM_022552 0.06 7 118 id6054 DNMT3A 2 25457185 SNP A T Missense_Mutation p.L901H c.27021 > A NM_022552 0.083333 6 66 id14602 DNMT3A 2 25457207 INS — C Frame_Shift_Ins p.W893fs c.2679_2680insG NM_022552 0.08 6 67 id12337 DNMT3A 2 25457218 SNP C T Missense_Mutation p.G890D c.2669G > A NM_022552 0.0625 3 45 id1006 DNMT3A 2 25457238 INS — A Frame_Shift_Ins p.L883fs c.2648_2649insT NM_022552 0.09 7 68 id3225 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.078947 3 35 id16125 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.320755 17 36 id3816 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.290323 27 66 id3392 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.231707 19 63 id14954 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.381818 21 34 id6065 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.302632 23 53 id87970 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.068966 4 54 id12364 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.210526 8 30 id14900 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.06383 3 44 id15861 DNMT3A 2 25457242 SNP C G Missense_Mutation p.R882P c.2645G > C NM_022552 0.136986 10 63 id3327 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.081081 6 68 id6943 DNMT3A 2 25457242 SNP C G Missense_Mutation p.R882P c.2645G > C NM_022552 0.087719 5 52 id9681 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.069767 3 40 id10973 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.153846 8 44 id13747 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.089888 8 81 id6895 DNMT3A 2 25457242 SNP C G Missense_Mutation p.R882H c.2645G > C NM_022552 0.183099 13 58 id3247 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.142857 12 72 id4970 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.202899 14 55 id15710 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.492958 35 36 id6805 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.084507 6 65 id8646 DNMT3A 2 25457242 SNP C G Missense_Mutation p.R882P c.2645G > C NM_022552 0.190476 12 51 id12236 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.136364 12 76 id6767 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.072727 4 51 id10899 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.136364 9 57 id14751 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.258621 15 43 id15615 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.169492 10 49 id15630 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.061728 5 76 id1921 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.065217 3 43 id15600 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.377358 20 33 id17014 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.078431 4 47 id9232 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.072727 4 51 id3491 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.090909 7 70 id4533 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.089744 7 71 id5608 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.285714 12 30 id1519 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.155556 7 38 id15436 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.211536 11 41 id5422 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > C NM_022552 0.057143 4 66 id2809 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.242105 23 72 id2743 DNMT3A 2 25457242 SNP C G Missense_Mutation p.R882P c.2645G > A NM_022552 0.107143 6 50 id5188 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.057143 4 66 id2698 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.113924 9 70 id12547 DNMT3A 2 25457242 SNP C T Missense_Mutation p.R882H c.2645G > A NM_022552 0.1 7 63 id2672 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2645G > T NM_022552 0.122449 6 43 id16177 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2645G > T NM_022552 0.076923 3 36 id16090 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2645G > T NM_022552 0.065574 4 57 id9965 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2645G > T NM_022552 0.075 3 37 id2320 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2645G > T NM_022552 0.102941 7 61 id15309 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2645G > T NM_022552 0.076923 3 36 id15857 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2645G > T NM_022552 0.135593 8 51 id13686 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2645G > T NM_022552 0.104167 5 43 id2059 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2645G > T NM_022552 0.226667 17 58 id3197 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2645G > T NM_022552 0.302326 13 30 id3170 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2645G > T NM_022552 0.065217 6 86 id4835 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2645G > T NM_022552 0.097561 8 74 id3681 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2645G > T NM_022552 0.205882 14 54 id14695 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2645G > T NM_022552 0.076923 5 60 id5592 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2645G > T NM_022552 0.117647 4 30 id1759 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2645G > T NM_022552 0.114286 4 31 id14601 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2644C > T NM_022552 0.064516 4 58 id3613 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2644C > T NM_022552 0.375 30 50 id4532 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2644C > T NM_022552 0.085714 3 32 id6567 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2644C > T NM_022552 0.066667 4 56 id13423 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2644C > T NM_022552 0.1 7 63 id5194 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2644C > T NM_022552 0.372881 22 37 id14241 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2644C > T NM_022552 0.175439 10 47 id10367 DNMT3A 2 25457243 SNP G A Missense_Mutation p.R882C c.2644C > T NM_022552 0.052632 4 72 id714 DNMT3A 2 25457245 SNP G A Missense_Mutation p.R882C c.2644C > T NM_022552 0.196078 10 41 id362 DNMT3A 2 25457249 SNP C A Missense_Mutation p.S881I c.2642G > T NM_022552 0.09434 5 48 id5767 DNMT3A 2 25457249 SNP T C Missense_Mutation p.M880V c.2638A > G NM_022552 0.266667 16 44 id2054 DNMT3A 2 25457249 SNP T C Missense_Mutation p.M880V c.2638A > G NM_022552 0.061538 4 61 id8376 DNMT3A 2 25457281 SNP C A Missense_Mutation p.G869V c.2606G > T NM_022552 0.083333 4 44 id3283 DNMT3A 2 25457283 DEL A — Frame_Shift_Del p.F868fs c.2604delT NM_022552 0.24 13 41 id9594 DNMT3A 2 25458593 SNP C T Nonsense_Mutation p.W860* c.2578T > C NM_022552 0.169811 9 44 id15965 DNMT3A 2 25458595 INS — T Frame_Shift_Ins p.L859fs c.2575.2576delTT NM_022552 0.07 8 111 id3141 DNMT3A 2 25458595 SNP A G Missense_Mutation p.W860R c.2578T > C NM_022552 0.288889 13 32 id16986 DNMT3A 2 25458595 SNP A G Missense_Mutation p.W860R c.2578T > C NM_022552 0.036232 5 133 id6619 DNMT3A 2 25458597 DEL AA — Frame_Shift_Del p.L859fs c.2575_2576delTT NM_022552 0.07 6 85 id2249 DNMT3A 2 25458602 DEL G — Frame_Shift_Del p.D857fs c.2571delC NM_022552 0.1 7 62 id9931 DNMT3A 2 25458622 DEL AG — Frame_Shift_Del p.V850fs c.2550_2551delCT NM_022552 0.14 11 66 id5711 DNMT3A 2 25458635 DEL CTGGT — Frame_Shift_Del p.D845fs c.2534_2538delAC NM_022552 0.21 15 57 id17135 CAG DNMT3A 2 25458653 INS — A Frame_Shift_Ins p.I840fs c.2519_2520insT NM_022552 0.14 12 71 id3494 DNMT3A 2 25458692 DEL G — Frame_Shift_Del p.F827fs c.2481delC NM_022552 0.16 18 93 id15890 DNMT3A 2 25458695 SNP C T Splice_Site c.e22 − 1 c.2479_splice NM_022552 0.071429 4 52 id15335 DNMT3A 2 25458696 SNP T C Splice_Site c.e22 − 1 c.2479_splice NM_022552 0.146667 11 64 id8979 DNMT3A 2 25458696 SNP T C Splice_Site c.e22 − 1 c.2479_splice NM_022552 0.051948 4 73 id15858 DNMT3A 2 25458696 SNP T C Splice_Site c.e22 − 1 c.2479_splice NM_022552 0.0625 4 60 id10900 DNMT3A 2 25458696 SNP T C Splice_Site c.e22 − 1 c.2479_splice NM_022552 0.103448 9 78 id11120 DNMT3A 2 25459804 SNP C T Splice_Site c.e21 + 1 c.2478_splice NM_022552 0.114286 4 31 id6005 DNMT3A 2 25459872 SNP G A Missense_Mutation p.P804L c.2411C > T NM_022552 0.018 9 41 id2965 DNMT3A 2 25462011 SNP G T Missense_Mutation p.P799H c.2396C > A NM_022552 0.166667 4 20 id13451 DNMT3A 2 25462015 DEL G — Frame_Shift_Del p.L798fs c.2392delC NM_022552 0.19 10 44 id15563 DNMT3A 2 25462020 SNP C A Missense_Mutation p.G796V c.2387G > T NM_022552 0.090909 4 40 id8952 DNMT3A 2 25462022 SNP C T Nonsense_Mutation p.W795* c.2385G > A NM_022552 0.12069 7 51 id3421 DNMT3A 2 25462053 DEL A — Frame_Shift_Del p.V785fs c.2354delT NM_022552 0.21 13 50 id3033 DNMT3A 2 25463171 SNP C G Missense_Mutation p.E774D c.2322G > C NM_022552 0.061069 8 123 id3224 DNMT3A 2 25463171 SNP C T Splice_Site c.e19 + 1 c.2322_splice NM_022552 0.487179 38 40 id12147 DNMT3A 2 25463175 SNP A C Missense_Mutation p.L773R c.23187 > G NM_022552 0.03937 5 122 id12726 DNMT3A 2 25463179 SNP A C Missense_Mutation p.F772V c.2314T > G NM_022552 0.038462 6 150 id17117 DNMT3A 2 25463181 SNP C T Missense_Mutation p.R771Q c.2312G > A NM_022552 0.076923 7 84 id3722 DNMT3A 2 25463181 SNP C T Missense_Mutation p.R771Q c.2312G > A NM_022552 0.05042 6 113 id1923 DNMT3A 2 25463181 SNP C T Missense_Mutation p.R771Q c.2312G > A NM_022552 0.055556 8 136 id3034 DNMT3A 2 25463181 SNP C T Missense_Mutation p.R771Q c.2312G > A NM_022552 0.044444 4 86 id1070 DNMT3A 2 25463182 SNP G A Nonsense_Mutation p.R7711* c.2312G > A NM_022552 0.051095 7 130 id16177 DNMT3A 2 25463182 SNP G A Nonsense_Mutation p.R7711* c.2312G > A NM_022552 0.223022 31 108 id9845 DNMT3A 2 25463182 SNP G A Nonsense_Mutation p.R7711* c.2312G > A NM_022552 0.191589 41 173 id6978 DNMT3A 2 25463182 SNP G A Nonsense_Mutation p.R7711* c.2312G > A NM_022552 0.088234 12 124 id10935 DNMT3A 2 25463182 SNP G A Nonsense_Mutation p.R7711* c.2312G > A NM_022552 0.065728 14 199 id4836 DNMT3A 2 25463182 SNP G A Nonsense_Mutation p.R7711* c.2312G > A NM_022552 0.04375 7 153 id2776 DNMT3A 2 25463182 SNP G A Nonsense_Mutation p.R7711* c.2312G > A NM_022552 0.112069 13 103 id14242 DNMT3A 2 25463182 SNP G A Nonsense_Mutation p.R7711* c.2312G > A NM_022552 0.224599 42 145 id8817 DNMT3A 2 25463184 SNP G C Missense_Mutation p.S770W c.2309C > G NM_022552 0.09434 10 96 id15610 DNMT3A 2 25463184 SNP G C Missense_Mutation p.S770W c.2309C > G NM_022552 0.046053 7 145 id15584 DNMT3A 2 25463185 SNP A G Missense_Mutation p.S770P c.2308T > C NM_022552 0.03876 5 124 id14786 DNMT3A 2 25463197 SNP T A Nonsense_Mutation p.K766* c.2296A > T NM_022552 0.049505 5 96 id8982 DNMT3A 2 25463208 DEL C — Frame_Shift_Del p.G762fs c.2285delG NM_022552 0.04 9 217 id2523 DNMT3A 2 25463211 DEL AT — Frame_Shift_Del p.M761fs c.2281_2282delAT NM_022552 0.28 38 96 id1600 DNMT3A 2 25463212 SNP T C Missense_Mutation p.M761V c.2281A > G NM_022552 0.047847 10 199 id2963 DNMT3A 2 25463213 DEL G — Frame_Shift_Del p.A760fs c.2280delC NM_022552 0.07 14 198 id9680 DNMT3A 2 25463228 SNP A C Missense_Mutation p.F755L c.22651 > G NM_022552 0.048276 7 138 id9897 DNMT3A 2 25463229 SNP A G Missense_Mutation p.F755S c.2264T > C NM_022552 0.247619 26 79 id16185 DNMT3A 2 25463229 SNP A G Missense_Mutation p.F755S c.2264T > C NM_022552 0.064286 9 131 id14878 DNMT3A 2 25463230 SNP A G Missense_Mutation p.F755S c.2263T > A NM_022552 0.0413103 5 111 id8657 DNMT3A 2 25463230 SNP A T Missense_Mutation p.F755I c.2263T > A NM_022552 0.121622 9 65 id16013 DNMT3A 2 25463230 SNP A T Missense_Mutation p.F755I c.2263T > A NM_022552 0.043478 6 132 id5386 DNMT3A 2 25463231 DEL G — Frame_Shift_Del p.L754fs c.2262delC NM_022552 0.34 41 78 id12579 DNMT3A 2 25463232 SNP A T Missense_Mutation p.L754H c.2261T > A NM_022552 0.070796 8 105 id9280 DNMT3A 2 25463235 SNP C T Nonsense_Mutation p.W753* c.2258G > A NM_022552 0.282258 35 89 id15952 DNMT3A 2 25463236 DEL AGA — In_Frame_Del p.F752del c.2255_2257delTCT NM_022552 0.04 6 142 id2365 DNMT3A 2 25463237 SNP G C Missense_Mutation p.F752L c.2256C > G NM_022552 0.0625 6 90 id8525 DNMT3A 2 25463238 DEL A — Frame_Shift_Del p.F752fs c.2255delT NM_022552 0.09 12 128 id3758 DNMT3A 2 25463239 SNP A C Missense_Mutation p.F752V c.2254T > G NM_022552 0.093168 15 146 id12121 DNMT3A 2 25463239 SNP A T Missense_Mutation p.F752I c.2254T > A NM_022552 0.165414 22 111 id13396 DNMT3A 2 25463239 SNP A C Missense_Mutation p.F752V c.2254T > G NM_022552 0.047945 7 139 id8192 DNMT3A 2 25463240 DEL G — Frame_Shift_Del p.F751fs c.2253delC NM_022552 0.07 9 117 id3805 DNMT3A 2 25463240 DEL G — Frame_Shift_Del p.F751fs c.2253delC NM_022552 0.04 7 184 id6954 DNMT3A 2 25463240 SNP G C Missense_Mutation p.F75IL c.2253C > G NM_022552 0.3125 35 77 id6937 DNMT3A 2 25463243 DEL G — Frame_Shift_Del p.750fs c.2250delC NM_022552 0.04 9 208 id4361 DNMT3A 2 25463246 DNP GC AA Missense_Mutation p.R749L c.2246_2247GC > TT NM_022552 0.302326 26 60 id8792 DNMT3A 2 25463247 SNP C T Missense_Mutation p.R749H c.2246G > A NM_022552 0.042857 6 134 id1756 DNMT3A 2 25463267 INS — CG Frame_Shift_Del p.R742fs c.2225_2226insCG NM_022552 0.23 21 72 id16702 DNMT3A 2 25463283 SNP A T Missense_Mutation p.L737H c.2210T > A NM_022552 0.11236 10 79 id9422 DNMT3A 2 25463283 SNP A C Missense_Mutation p.L737P c.2210T > C NM_022552 0.070175 12 159 id6671 DNMT3A 2 25463283 SNP A C Missense_Mutation p.L737R c.22101 > G NM_022552 0.064103 5 73 id6440 DNMT3A 2 25463286 SNP C T Missense_Mutation p.R736H c.22070 > A NM_022552 0.035971 5 134 id2222 DNMT3A 2 25463286 SNP C T Missense_Mutation p.R736H c.2207G > A NM_02552 0.206897 12 46 id15683 DNMT3A 2 25463286 SNP C T Missense_Mutation p.R736H c.2207G > A NM_022552 0.130841 14 93 id9230 DNMT3A 2 25463286 SNP C G Missense_Mutation p.R736F c.22070 > C NM_022552 0.051282 6 111 id2964 DNMT3A 2 25463286 SNP C T Missense_Mutation p.R736H c.2207G > A NM_022552 0.202703 15 59 id1522 DNMT3A 2 25463287 SNP G A Missense_Mutation p.R736C c.2206C > T NM_022552 0.069767 6 80 id10935 DMMT3A 2 25463287 DEL G — Frame_Shift_Del p.R736fs c.2206delC NM_022552 0.04 7 151 id4834 DNMT3A 2 25463287 SNP G A Missense_Mutalion p.R736C c.2206C > T NM_022552 0.045455 5 105 id1400 DNMT3A 2 25463287 SNP G A Missense_Mutation p.R736C c.2206C > T NM_022552 0.107143 9 75 idl1823 DNMT3A 2 25463287 SNP G A Missense_Mutation p.R736C c.2206C > T NM_022552 0.066667 4 56 id8297 DNMT3A 2 25463288 INS — A Frame_Shift_Ins p.Y735fs c.2204_2205insT NM 022552 0.07 6 85 id3425 DNMT3A 2 25463288 SNP G C Nonsense_Mutation p.Y735* c.2204_2205insT NM_022552 0.122807 7 50 id3119 DNMT3A 2 25463289 SNP T C Missense_Mutation p.Y735C c.2204A > G NM_022552 0.060606 6 93 id6953 DNMT3A 2 25463289 SNP T C Missense_Mutation p.Y735C c,2204A > G NM_022552 0.0625 6 90 id14999 DNMT3A 2 25463289 SNP T C Missense_Mutation p.Y735C c.2204A > G NM_022552 0.125 11 77 id12769 DNMT3A 2 25463289 SNP T C Missense_Mutation p.Y735C c.2204A > G NM_022552 0.15493 11 60 id8729 DNMT3A 2 25463289 SNP T C Missense_Mutation p.Y735C c.2204A > G NM_022552 0.068966 6 81 id13765 DNMT3A 2 25463289 SNP T G Missense_Mutation p.Y735C c.2204A > C NM_022552 0.044444 4 86 id3658 DNMT3A 2 25463289 SNP T C Missense_Mutation p.Y735C c.2204A > G NM_022552 0.14 7 43 id15574 DNMT3A 2 25463291 SNP G C Missense_Mutation p.F734L c.2202C > G NM_022552 0.045455 5 105 id2350 DNMT3A 2 25463292 SNP A C Missense_Mutation p.F734C c.2201T > G NM_022552 0.1 8 72 id11518 DNMT3A 2 25463296 INS — A Frame_Shift_Ins p.F732fs c.2196_2197insT NM_022552 0.09 9 88 id15312 DNMT3A 2 25463298 SNP A G Missense_Mutation p.F732S c.2195T > C NM_022552 0.084746 5 54 id8808 DNMT3A 2 25463298 DEL AAG — In_Frame_Del p.731_732FF > F c.2193_2195delCTT NM_022552 0.05 7 124 id3448 DNMT3A 2 25463298 SNP A G Missense_Mutation p.F732S c.2195T > C NM_022552 0.092105 7 69 id9932 DNMT3A 2 25463298 DEL AAG — In_Frame_Del p.731_732FF > F c.2193_2195delCTT NM_022552 0.04 6 155 id6823 DNMT3A 2 25463298 DEL AAG — In_Frame_Del p.731_732FF > F c.2193_2195delCTT NM_022552 0.21 19 73 id5924 DNMT3A 2 25463299 DEL AGAAG — Frame_Shift_Del p.L730fs c.2190_2194delCTTCT NM_022552 0.08 8 88 id8675 DNMT3A 2 25463300 SNP G T Missense_Mutation p.F731L c.2193C > A NM_022552 0.067797 4 55 id9875 DNMT3A 2 25463307 SNP C T Missense_Mutation p.R729Q c.2186G > A NM_022552 0.053763 5 88 id5320 DNMT3A 2 25463308 SNP G A Missense_Mutation p.R729W c.2185C > T NM_022552 0.098592 7 64 id2396 DNMT3A 2 25463308 SNP G A Missense_Mutation p.R729W c.2185C > T NM_022552 0.088889 4 41 id16068 DNMT3A 2 25463308 SNP G A Missense_Mutation p.R729W c.2185C > T NM_022552 0.098361 6 55 id5009 DNMT3A 2 25463308 SNP G A Missense_Mutation p.R729W c.2185C > T NM_022552 0.05 4 76 id8983 DNMT3A 2 25463308 SNP G C Missense_Mutation p.R729G c.2185C > G NM_022552 0.088608 7 72 id6003 DNMT3A 2 25463308 SNP G A Missense_Mutation p.R729W c.2185C > T NM_022552 0.051948 4 73 id2025 DNMT3A 2 25463308 SNP G A Missense_Mutation p.R729W c.2185C > T NM_022552 0.074074 6 75 id15128 DNMT3A 2 25463320 SNP C T Splice_Site c.e19 − 1 c.2174_splice NM_022552 0.040404 4 95 id2319 DNMT3A 2 25463320 SNP C T Splice_Site c.e19 − 1 c.2174_splice NM_022552 0.052632 4 72 id14459 DNMT3A 2 25463508 SNP C T Splice_Site c.e19 − 1 c.2174_splice NM_022552 0.072464 5 64 id1939 DNMT3A 2 25463508 SNP C T Splice_Site c.e19 − 1 c.2174_splice NM_022552 0.04902 5 97 id5800 DNMT3A 2 25463508 SNP C T Splice_Site c.e19 − 1 c.2174_splice NM_022552 0.057143 6 99 id7308 DNMT3A 2 25463508 SNP C T Splice_Site c.e19 − 1 c.2174_splice NM_022552 0.054054 6 105 id1242 DNMT3A 2 25463508 SNP C T Splice_Site c.e19 − 1 c.2174_splice NM_022552 0.037594 10 256 id6108 DNMT3A 2 25463509 SNP C T Splice_Site c.e19 − 1 c.2174_splice NM_022552 0.054795 4 69 id1343 DNMT3A 2 25463523 SNP C T Missense_Mutation p.R720H c.2159G > A NM_022552 0.068323 11 150 id8630 DNMT3A 2 25463529 DEL G — Frame_Shift_Del p.P718fs c.2153delC NM_022552 0.06 8 136 id12206 DNMT3A 2 25463532 SNP T A Missense_Mutation p.N7171 c.2150A > T NM_022552 0.079812 17 196 id6708 DNMT3A 2 25463541 SNP G C Missense_Mutation p.S714C c.2141C > G NM_022552 0.036765 5 131 id16144 DNMT3A 2 25463541 SNP G C Missense_Mutation p.S714C c.2141C > G NM_022552 0.042553 4 90 id15164 DNMT3A 2 25463541 SNP G C Missense_Mutation p.S714C c.2141C > G NM_022552 0.055556 5 85 id14273 DNMT3A 2 25463554 SNP A T Missense_Mutation p.C710S c.2128T > A NM_022552 0.125926 17 118 id3426 DNMT3A 2 25463554 SNP A T Missense_Mutation p.C710S c.2128T > A NM_022552 0.045161 7 148 id4883 DNMT3A 2 25463568 SNP A G Missense_Mutation p.I705T c.2114T > C NM_022552 0.044444 4 86 id13787 DNMT3A 2 25463568 SNP A G Missense_Mutation p.I705T c.2114T > C NM_022552 0.103774 11 95 id8691 DNMT3A 2 25463571 SNP A C Missense_Mutation p.V704G c.2111T > G NM_022552 0.042373 5 113 id12386 DNMT3A 2 25463578 SNP C A Missense_Mutation p.D702Y c.2104G > T NM_022552 0.062069 9 136 id16127 DNMT3A 2 25463578 SNP C T Missense_Mutation p.D702N c.2104G > A NM_022552 0.139241 11 68 id8614 DNMT3A 2 25463583 DEL G — Frame_Shift_Del p.P700fs c.2099delC NM_022552 0.07 6 80 id16162 DNMT3A 2 25463583 DEL G C Missense_Mutation p.P700R c.2099C > G NM_022552 0.059524 5 79 id8775 DNMT3A 2 25463584 SNP G A Missense_Mutation p.P700S c.2098C > T NM_022552 0.338028 24 47 id15026 DNMT3A 2 25463586 SNP C T Missense_Mutation p.G699D c.2096G > A NM_022552 0.17284 14 67 id1719 DNMT3A 2 25463587 SNP C T Missense_Mutation p.G699S c.2095G > A NM_022552 0.116667 7 53 id3790 DNMT3A 2 25463587 SNP C T Missense_Mutation p.G699S c.2095G > A NM_022552 0.207207 23 88 id3196 DNMT3A 2 25463587 SNP C T Missense_Mutation p.G699S c.2095G > A NM_022552 0.226667 17 58 id12570 DNMT3A 2 25463596 SNP G A Nonsense_Mutation p.Q696* c.2086C > T NM_022552 0.275 44 116 id9013 DNMT3A 2 25463601 SNP T C Splice_Site c.e18 − 1 c.2083_splice NM_022552 0.197183 14 57 id2365 DNMT3A 2 25463601 SNP T C Splice_Site c.e18 − 1 c.2083_splice NM_022552 0.129032 8 54 id5795 DNMT3A 2 25464428 INS — A Splice_Site c.e17 + 1 c.2082_splice NM_022552 0.28 11 29 id2306 DNMT3A 2 25464429 SNP A C Splice_Site c.e17 + 1 c.2082_splice NM_022552 0.63492 4 59 id15906 DNMT3A 2 25464430 SNP C T Splice_Site c.e17 + 1 c.2082_splice NM_022552 0.104167 5 43 id16591 DNMT3A 2 25464456 SNP T A Missense_Mutation p.D686V c.2057A > T NM_022552 0.122807 7 50 id9977 DNMT3A 2 25464457 SNP C A Missense_Mutation p.D686Y c.2056G > T NM_022552 0.083333 4 44 id12975 DNMT3A 2 25464459 SNP C T Missense_Mutation p.G685E c.20540G > A NM_022552 0.114286 4 31 id3840 DNMT3A 2 25464459 SNP C G Missense_Mutation p.G685E c.2054G > C NM_022552 0.083333 4 44 id11374 DNMT3A 2 25464460 SNP C T Missense_Mutation p.G685R c20536G > A NM_022552 0.080645 5 57 id14857 DNMT3A 2 25464486 SNP C T Missense_Mutation p.R676Q c.2027G > A NM_022552 0.102041 5 44 id16161 DNMT3A 2 25464505 INS — G Frame_Shift_Ins p.5669fs c.20007_2008insC NM_022552 0.27 13 36 id16841 DNMT3A 2 25464534 SNP T C Missense_Mutation p.Y060C c:1979A > G NM_022552 0.075 3 37 id4444 DNMT3A 2 25464537 SNP C T Missense_Mutation p.R659H c.1976G > A NM_022552 0.238095 5 16 id16060 DNMT3A 2 25464537 SNP C T Missense_Mutation p.R659H c.1976G > A NM_022552 0.066667 3 42 id17165 DNMT3A 2 25464544 SNP C T Missense_Mutation p.V657M c.1969G > A NM_022552 0.136364 3 19 id14784 DNMT3A 2 25464544 SNP C T Missense_Mutation p.V657M c.1969G > A NM_022552 0.111111 3 24 id3950 DNMT3A 2 25464544 SNP C T Missense_Mutation p.V657M c.1969G > A NM_022552 0.117647 6 45 id8852 DNMT3A 2 25464547 SNP G A Nonsense_Mutation p.Q656* c1966C > T NM_022552 0.125 4 28 id11568 DNMT3A 2 25464554 SNP C A Missense_Mutation p.L653F C.1959G > T NM_022552 0.195122 8 33 id2375 DNMT3A 2 25467022 SNP A G Splice_Site c.e15 + 1 c.1851_splice NM_022552 0.196078 10 41 id13422 DNMT3A 2 25467023 SNP C T Splice_Site c.e15 + 1 c.1851_splice NM_022552 0.0625 4 60 id6567 DNMT3A 2 25467032 SNP G A Nonsense_Mutation p.Q615* c.1843C > T NM_022552 0.074527 5 62 id14957 DNMT3A 2 25467065 DEL G — Frame_Shift_Del p.R604fs c.1810delC NM_022552 0.12 6 43 id17044 DNMT3A 2 25467073 SNP C T Nonsense_Mutation p.W601* c.1802G > A NM_022552 0.117647 8 60 id8648 DNMT3A 2 25467083 SNP G A Nonsense_Mutation p.R598* c.1792C > T NM_022552 0.225806 14 48 id16194 DNMT3A 2 25467083 SNP G A Nonsense_Mutation p.R598* c.1792C > T NM_022552 0.083333 3 33 id14858 DNMT3A 2 25467083 SNP G A Nonsense_Mutation p.R598* c.1792C > T NM_022552 0.086207 5 53 id2009 DNMT3A 2 25467083 SNP G A Nonsense_Mutation p.R598* c.1792C > T NM_022552 0.246154 16 49 id14719 DNMT3A 2 25467083 SNP G A Nonsense_Mutation p.R598* c.1792C > T NM_022552 0.06383 3 44 id13566 DNMT3A 2 25467083 SNP G A Nonsense_Mutation p.R598* c.1792C > T NM_022552 0.105263 8 68 id2962 DNMT3A 2 25467083 SNP G A Nonsense_Mutation p.R598* c.1792C > T NM_022552 0.069767 3 40 id11375 DNMT3A 2 25467111 DEL G — Frame_Shift_Del p.H588fs c.1764delC NM_022552 0.24 15 47 id15339 DNMT3A 2 25467132 SNP C T Nonsense_Mutation p.W581* c.1741T > G NM_022552 0.282051 11 28 id6869 DNMT3A 2 25467134 SNP A C Missense_Mutation p.W581G c.1741T > G NM_022552 0.115385 3 23 id16011 DNMT3A 2 25467407 SNP A C Splice_Site c.e14 + 1 c.1667_splice NM_022552 0.090909 4 40 id3119 DNMT3A 2 25467408 SNP C A Splice_Site c.e14 + 1 c.1667_splice NM_022552 0.0625 4 60 id8406 DNMT3A 2 25467408 SNP C G Splice_Site c.e14 + 1 c.1667_splice NM_022552 0.215686 11 40 id2828 DNMT3A 2 25467428 SNP C T Missense_Mutation p.G550R c.1648G > A NM_022552 0.127273 7 48 id2304 DNMT3A 2 25467428 SNP C T Missense_Mutation p.G550R c.1648G > A NM_022552 0.065217 3 43 id15052 DNMT3A 2 25467436 SNP A T Missense_Mutation p.L547H c.1640T > A NM_022552 0.129412 11 74 id14965 DNMT3A 2 25467436 SNP A C Missense_Mutation p.L547R c.1640T > G NM_022552 0.083333 3 33 id8962 DNMT3A 2 25467436 SNP A T Missense_Mutation p.L547H c.1640T > A NM_022552 0.107692 7 58 id12146 DNMT3A 2 25467437 SNP G A Missense_Mutation p.L547F c.1639C > T NM_022552 0.25 19 57 id9916 DNMT3A 2 25467437 SNP G A Missense_Mutation p.L547F c.1639C > T NM_022552 0.173077 9 43 id8674 DNMT3A 2 25467447 INS — C Frame_Shift_Ins p.G543fs c.1628_1629insG NM_022552 0.05 6 105 id2607 DNMT3A 2 25467448 SNP C G Missense_Mutation p.G543A c.1628G > C NM_022552 0.052632 4 72 id2180 DNMT3A 2 25467449 SNP C A Missense_Mutation p.G543C c.1627G > T NM_022552 0.140845 10 61 id5941 DNMT3A 2 25467449 SNP C T Missense_Mutation p.G543S c.1627G > A NM_022552 0.08 6 69 id10674 DNMT3A 2 25467465 SNP G T Nonsense_Mutation p.C537* c.1611C > A NM_022552 0.111111 11 88 id1774 DNMT3A 2 25467468 SNP G T Nonsense_Mutation p.Y536* c.1608C > A NM_022552 0.126582 10 69 id14901 DNMT3A 2 25467471 DEL G — Frame_Shift_Del p.G532fs c.1605delC NM_022552 0.22 21 74 id3357 DNMT3A 2 25467476 SNP G A Nonsense_Mutation p.S535fs c.1600C > T NM_022552 0.047059 4 81 id1471 DNMT3A 2 25467481 DEL C — Frame_Shift_Del p.Q534* c. 1595delG NM_022552 0.06 7 112 id1672 DNMT3A 2 25467492 SNP G C Nonsense_Mutation p.G532fs c.1582_1583delTA NM_022552 0.078947 3 35 id16331 DNMT3A 2 25467493 DEL TA — Frame_Shift_Del p.Y528* c.1582_1583delTA NM_022552 0.11 8 68 id7028 DNMT3A 2 25467496 SNP T G Missense_Mutation P.Y528fs c.1580A > C NM_022552 0.123288 9 64 id2858 DNMT3A 2 25467497 SNP G A Nonsense_Mutation p.Q527P c.1580A > C NM_022552 0.160714 9 47 id15539 DNMT3A 2 25467503 DEL CA — Frame_Shift_Del p.Q527* c. 1572_1573delTG NM_022552 0.12 6 43 id2056 DNMT3A 2 25467522 SNP C G Splice_Site c.e14 − 1 c.1555_splice NM_022552 0.194805 15 62 id3359 DNMT3A 2 25467523 SNP T C Splice_Site c.e14 − 1 c.1555_splice NM_022552 0.050505 5 94 id4534 DNMT3A 2 25468121 SNP C T Splice_Site c.e13 + 1 c.1555_splice NM_022552 0.093023 8 78 id14835 DNMT3A 2 25468121 SNP C T Splice_Site c.e13 + 1 c.1555_splice NM_022552 0.095238 4 38 id1870 DNMT3A 2 25468128 INS — T Frame_Shift_Ins p.N516fs c.1547_1548insA NM_022552 0.11 10 79 id12758 DNMT3A 25468145 C A Nonsense_Mutation p.G511* p1531G > T NM_022552 0.047619 6 120 id5891 DNMT3A 2 25468153 INS — G Frame_Shift_Ins p.L508fs c.1522_1523insC NM_022552 0.1 12 106 id14752 DNMT3A 2 25468165 DEL A — Frame_Shift_Del p.L504fs c.1511delT NM_022552 0.1 8 73 id2118 DNMT3A 2 25468181 DEL T — Frame_Shift_Del p.S4991fs c.1495delA NM_022552 0.07 11 137 id5858 DNMT3A 2 25468182 DEL C — Frame_Shift_Del p.6498fs c.1494delG NM_022552 0.08 10 121 id3360 DNMT3A 2 25468186 SNP C T Missense_Mutation p.C4971 c.14906 > A NM_022552 0.104348 12 103 id9043 DNMT3A 2 25468202 SNP C G Splice_Site c.e13 − 1 c.1475_splice NM_022552 0.04 4 96 id3227 DNMT3A 2 25468203 DEL T — Splice_Site c.e13 − 1 c.1475_splice NM_022552 0.06 6 99 id4969 DNMT3A 2 25468887 SNP A C Splice_Site c.e13 − 1 c.1475_splice NM_022552 0.060606 6 93 id2258 DNMT3A 2 25468899 DEL C — Frame_Shift_Del p.R488fs c.1464delG NM_022552 0.07 7 92 id7288 DNMT3A 2 25468899 DEL C — Frame_Shift_Del p.R488fs c.1464delG NM_022552 0.06 6 102 id1412 DNMT3A 2 25468907 SNP T A Nonsense_Mutation p.K486* c.1456A > T NM_022552 0.06 6 94 id15373 DNMT3A 2 25468919 SNP C A Nonsense_Mutation p.E482* c.1444G > T NM_022552 0.184211 14 52 id2235 DNMT3A 2 25468935 SNP C T Splice_Site c.e12 − 1 c.1430_splice NM_022552 0.102041 5 44 id15827 DNMT3A 2 25469028 SNP C T Splice_Site c.e12 − 1 c.1430_splice NM_022552 0.055556 5 85 id16096 DNMT3A 2 25469028 SNP C T Splice_Site c.e12 − 1 c.1430_splice NM_022552 0.055556 6 102 id3449 DNMT3A 2 25469028 SNP C T Splice_Site c.e12 − 1 c.1430_splice NM_022552 0.046154 6 124 id9784 DNMT3A 2 25469028 SNP C T Splice_Site c.e12 − 1 c.1430_splice NM_022552 0.038095 8 202 id4837 DNMT3A 2 25469041 SNP C A Nonsense_Mutation p.E473* c.14176 > T NM_022552 0.089655 13 132 id6770 DNMT3A 2 25469048 DEL A — Frame_Shift_Del p.1470fs c.1410delT NM_022552 0.11 15 118 id16080 DNMT3A 2 25469063 DEL G — Frame_Shift_Del p.P465fs c.1395delC NM_022552 0.05 7 138 id9719 DNMT3A 2 25469083 SNP T A Nonsense_Mutation p.K459* c.1375A > T NM_022552 0.041096 9 210 id925 DNMT3A 2 25469096 DEL GG — Frame_Shift_Del p.A454fs c.1361_1362delCC NM_022552 0.24 56 175 id3344 DNMT3A 2 25469154 DEL ACTT — Frame_Shift_Del p.E434fs c.1301_1304delAAGT NM_022552 0.06 24 360 id6788 DNMT3A 2 25469156 DEL T — Frame_Shift_Del p.E434fs c.1301_1304delAAGT NM_022552 0.03 6 190 id68483 DNMT3A 2 25469165 DEL GGGATTCTT — Frame_Shift_Del p.E434fs c.1281_1293delAG NM_022552 0.09 19 198 id16176 CTCT AGAAGAATCCC DNMT3A 2 25469488 SNP C T Splice_Site c.e10 + 1 c.1279_splice NM_022552 0.060976 5 77 id4443 DNMT3A 2 25469527 SNP A C Missense_Mutation p.F414C c.1241T > G NM_022552 0.07563 9 110 id9420 DNMT3A 2 25469527 SNP A C Missense_Mutation p.F414C c.1241T > G NM_022552 0.090909 11 110 id10738 DNMT3A 2 25469541 SNP C T Nonsense_Mutation p.W409* c.1227G > A NM_022552 0.117647 12 90 id6084 DNMT3A 2 25469541 SNP C T Nonsense_Mutation p.W409* c.1227G > A NM_022552 0.043103 5 11 id12467 DNMT3A 2 25469541 SNP C T Nonsense_Mutation p.W409* c.1227G > A NM_022552 0.079365 10 116 id1860 DNMT3A 2 25469542 DEL CATT — Frame_Shift_Del p.E408fs c.1223_1226delAATG NM_022552 0.11 10 81 id4929 DNMT3A 2 25469553 DEL CG — Frame_Shift_Del p.P405fs c.1214_1215delCC NM_022552 0.08 9 108 id15015 DNMT3A 2 25469564 SNP G A Nonsense_Mutation p.Q402* c.1204C > T NM_022552 0.045752 7 146 id8920 DNMT3A 2 25469564 SNP G A Nonsense_Mutation p.Q402* c.1204C > T NM_022552 0.131868 12 79 id3510 DNMT3A 2 25469566 INS — T Frame_Shift_Ins p.V401fs c.1201_1202insA NM_022552 0.08 14 171 id2808 DNMT3A 2 25469607 INS — CA Frame_Shift_Ins p.C387fs c.1160_1161insTG NM_022552 0.04 10 266 id8934 DNMT3A 2 25469613 INS — G Frame_Shift_Ins p.P385fs c.1154delC NM_022552 0.06 20 322 id16036 DNMT3A 2 25469614 DEL G — Frame_Shift_Del p.P385fs c.1154delC NM_022552 0.08 21 239 id8994 DNMT3A 2 25469614 DEL G — Frame_Shift_Del p.P385fs c.1154delC NM_022552 0.05 11 218 id6559 DNMT3A 2 25469625 DEL C — Frame_Shift_Del p.G381fs c.1143delG NM_022552 0.04 11 253 id15874 DNMT3A 2 25469625 DEL C — Frame_Shift_Del p.G381fs c.1143delG NM_022552 0.12 36 274 id15866 DNMT3A 2 25469646 SNP C G Splice_Site c.e10 − 1 c.1123_splice NM_022552 0.060241 5 78 id12659 DNMT3A 2 25469647 SNP T C Splice_Site c.e10 − 1 c.1123_splice NM_022552 0.072464 10 128 id13854 DNMT3A 2 25469919 SNP C T Splice_Site c.e9 + 1 c.1122_splice NM_022552 0.115385 3 23 id6691 DNMT3A 2 25469919 SNP C T Splice_Site c.e9 + 1 c.1122_splice NM_022552 0.290323 9 22 id16501 DNMT3A 2 25469919 SNP G A Nonsense_Mutation p.Q374* c.1121A > C NM_022552 0.133333 6 39 id17176 DNMT3A 2 25469922 SNP G A Nonsense_Mutation p.Q374* c.1121A > C NM_022552 0.190476 4 17 id11908 DNMT3A 2 25469945 SNP C T Missense_Mutation p.R366H c.1097G > A NM_022552 0.083333 3 33 id10859 DNMT3A 2 25469945 SNP C T Missense_Mutation p.R366H c.1097G > A NM_022552 0.078947 3 35 id10082 DNMT3A 2 25469946 SNP G C Missense_Mutation p.R366G c.1096C > G NM_022552 0.073171 3 38 id4535 DNMT3A 2 25469976 SNP G A Nonsense_Mutation p.Q356* c.1066C > T NM_022552 0.092593 5 49 id9595 DNMT3A 2 25469976 SNP G A Nonsense_Mutation p.Q356* c.1066C > T NM_022552 0.087912 8 83 id890 DNMT3A 2 25469984 DEL G — Frame_Shift_Del p.A353fs c.1058delC NM_022552 0.21 14 52 id15877 DNMT3A 2 25470029 INS — G Splice_Site c.e9 − 1 c.1015_splice NM_022552 0.41 9 13 id12897 DNMT3A 2 25470467 DEL A — Frame_Shift_Del p.F336fs c.1007delT NM_022552 0.03 8 246 id12009 DNMT3A 2 25470480 SNP C A Nonsense_Mutation p.G332* c.994G > T NM_022552 0.078652 7 82 id6025 DNMT3A 2 25470484 SNP C T Nonsense_Mutation p.W330* c.989_991delGGT NM_022552 0.066038 7 99 id5896 DNMT3A 2 25470485 SNP C T Nonsense_Mutation p.W330* c.989_991delGGT NM_022552 0.064516 6 87 id12786 DNMT3A 2 25470493 SNP C T Nonsense_Mutation p.W330* c.981G > A NM_022552 0.077519 10 119 id5022 DNMT3A 2 25470493 SNP C T Nonsense_Mutation p.W327* c.981G > A NM_022552 0.049587 6 115 id928 DNMT3A 2 25470494 SNP C T Missense_Mutation p.W327* c.981G > A NM_022552 0.103448 12 104 id15000 DNMT3A 2 25470497 SNP C T Missense_Mutation p.R326H c.977G > A NM_022552 0.082569 9 100 id9718 DNMT3A 2 25470497 SNP C T Missense_Mutation p.R326H c.977G > A NM_022552 0.044944 4 85 id12336 DNMT3A 2 25470497 SNP C T Missense_Mutation p.R326L c.977G > T NM_022552 0.25 21 63 id4839 DNMT3A 2 25470497 SNP C T Missense_Mutation p.R326H c.977G > A NM_022552 0.059524 5 79 id12997 DNMT3A 2 25470497 SNP C T Missense_Mutation p.R326H c.977G > A NM_022552 0.062602 20 103 id7161 DNMT3A 2 25470498 SNP G A Missense_Mutation p.R326C c.976C > T NM_022552 0.042105 4 91 id11044 DNMT3A 2 25470498 SNP G A Missense_Mutation p.R326C c.976C > T NM_022552 0.045455 4 84 id2197 DNMT3A 2 25470498 SNP G A Missense_Mutation p.R326C c.976C > T NM_022552 0.229885 20 67 id17118 DNMT3A 2 25470498 SNP G A Missense_Mutation p.R326C c.976C > T NM_022552 0.044944 4 85 id4694 DNMT3A 2 25470498 SNP G A Missense_Mutation p.R326C c.976C > T NM_022552 0.04902 5 97 id1859 DNMT3A 2 25470498 SNP G A Missense_Mutation p.R326C c.976C > T NM_022552 0.193548 18 75 id2999 DNMT3A 2 25470498 SNP G A Missense_Mutation p.R326C c.976C > T NM_022552 0.045455 4 84 id13251 DNMT3A 2 25470498 SNP G A Missense_Mutation p.R326C c.976C > T NM_022552 0.04878 4 78 id1279 DNMT3A 2 25470498 SNP G A Missense_Mutation p.R326C c.976C > T NM_022552 0.236842 27 87 id4044 DNMT3A 2 25470516 SNP G A Nonsense_Mutation p.R320* c.958C > T NM_022552 0.156863 8 43 id2402 DNMT3A 2 25470516 SNP G A Nonsense_Mutation p.R320* c.958C > T NM_022552 0.333333 21 42 id14930 DNMT3A 2 25470516 SNP G A Nonsense_Mutation p.R320* c.958C > T NM_022552 0.051282 6 111 id15764 DNMT3A 2 25470516 SNP G A Nonsense_Mutation p.R320* c.958C > T NM_022552 0.067416 6 83 id11418 DNMT3A 2 25470516 SNP G A Nonsense_Mutation p.R320* c.958C > T NM_022552 0.054054 6 105 id13315 DNMT3A 2 25470516 SNP G A Nonsense_Mutation p.R320* c.958C > T NM_022552 0.102804 11 96 id1472 DNMT3A 2 25470516 SNP G A Nonsense_Mutation p.R320* c.958C > T NM_022552 0.058824 5 80 id5130 DNMT3A 2 25470516 SNP G A Nonsense_Mutation p.R320* c.958C > T NM_022552 0.061404 7 107 id7185 DNMT3A 2 25470516 SNP G A Nonsense_Mutation p.R320* c.958C > T NM_022552 0.112676 8 63 id12510 DNMT3A 2 25470532 SNP C T Nonsense_Mutation p.W314* c.942G > A NM_022552 0.05 4 76 id14163 DNMT3A 2 25470555 SNP G A Missense_Mutation p.P307S c.919C > T NM_022552 0.074627 5 62 id5900 DNMT3A 2 25470556 SNP C T Nonsense_Mutation p.W306* c.9161 > C NM_022552 0.060241 5 78 id10739 DNMT3A 2 25470559 SNP C T Nonsense_Mutation p.W305* c.915G > A NM_022552 0.057971 4 65 id5942 DNMT3A 2 25470559 SNP C T Nonsense_Mutation p.W305* c.915G > A NM_022552 0.045977 4 83 id1145 DNMT3A 2 25470560 SNP C T Nonsense_Mutation p.W305* c.915G > A NM_022552 0.326087 15 31 id3438 DNMT3A 2 25470583 SNP C T Nonsense_Mutation p.W297* c.889T > G NM_022552 0.056075 6 101 id12120 DNMT3A 2 25470594 DEL C — Frame_Shift_Del p.E294fs c.880delG NM_022552 0.2 23 90 id8730 DNMT3A 2 25470594 SNP C A Nonsense_Mutation p.E294* c.880delG NM_022552 0.186047 16 70 id1768 DNMT3A 2 25470904 SNP A C Splice_Site c.e7 + 1 c.855_splice NM_022552 0.097561 4 37 id4692 DNMT3A 2 25470905 SNP C T Splice_Site c.e7 + 1 c.855_splice NM_022552 0.222222 10 35 id11643 DNMT3A 2 25470905 SNP C T Splice_Site c.e7 + 1 c.855_splice NM_022552 0.081967 5 56 id16034 DNMT3A 2 25470905 SNP C A Splice_Site c.e7 + 1 c.855_splice NM_022552 0.121212 4 29 id6449 DNMT3A 2 25470955 INS — C Frame_Shift_Ins p.A269fs c.805_806insG NM_022552 0.13 8 52 id1868 DNMT3A 2 25470982 DEL GT — Frame_Shift_Del p.T260fs c.778_779delAC NM_022552 0.06 13 192 id3379 DNMT3A 2 25471011 DEL G — Frame_Shift_Del p.P250fs c.750delC NM_022552 0.25 27 80 id15727 DNMT3A 2 25471016 SNP G A Nonsense_Mutation p.Q249* c.745C > T NM_022552 0.147059 15 87 id12637 DNMT3A 2 25471016 SNP G A Nonsense_Mutation p.Q249* c.745C > T NM_022552 0.047619 6 120 id5252 DNMT3A 2 25471030 DEL G — Frame_Shift_Del p.P244fs c.731delC NM_022552 0.14 15 92 id5902 DNMT3A 2 25471030 DEL G — Frame_Shift_Del p.P244fs c.731delC NM_022552 0.06 9 147 id14666 DNMT3A 2 25471039 DEL T — Frame_Shift_Del p.E241fs c.722delA NM_022552 0.06 7 106 id3226 DNMT3A 2 25471040 SNP C A Nonsense_Mutation p.E241* c.722delA NM_022552 0.082474 8 89 id6851 DNMT3A 2 25471048 INS — TC Frame_Shift_Ins p.K238fs c.712_713insGA NM_022552 0.06 8 126 id9185 DNMT3A 2 25471052 INS — A Frame_Shift_Ins p.S236fs c.708_709ins NM_022552 0.05 6 121 id2345 DNMT3A 2 25471069 DEL T — Frame_Shift_Del p.Q231fs c.692delA NM_022552 0.07 8 112 id17166 DNMT3A 2 25471082 DEL C — Frame_Shift_Del p.V227fs c.679delG NM_022552 0.26 18 51 id11008 DNMT3A 2 25523009 SNP G A Splice_Site c.e3 + 1 c.177_splice NM_022552 0.407407 11 16 id8961 EP300 22 41523642 INS — C Frame_Shift_Ins p.R353fs c.1058_1059insC NM_001429 0.08 21 231 id4590 ETV6 12 12022502 DEL C — Frame_Shift_Del p.S203fs c.608delC NM_001987 0.03 6 194 id3843 EZH2 7 148506461 SNP C T Missense_Mutation p.R679H c.2036G > A NM_001203247 0.047297 7 141 id15352 EZH2 7 148515097 INS — T Frame_Shift_Ins p.S366fs c.1096_1097insA NM_001203247 0.04 8 177 id6929 FAM46C 1 118165768 INS — C Frame_Shift_Ins p.L93fs c.278_279insC NM_017709 0.14 20 122 id16047 FBXW7 4 153268225 INS — A Splice_Site c.e4 − 1 c.585_splice NM_033632 0.12 6 44 id6058 FLT3 13 28608281 SNP A G Missense_Mutation p.V592A c.1775T > C NM_004119 0.066667 7 98 id6574 FOXP1 3 71026170 INS — T Frame_Shift_Ins p.K484fs c.1451 _1452insA NM_032682 0.05 7 134 id15631 GNAS 20 57484420 SNP C G Missense_Mutation p.R844G c.2530C > G NM_016592 0.260504 31 88 id12468 GNAS 20 57484420 SNP C T Missense_Mutation p.R844G c.2530C > T NM_016592 0.128205 10 68 id9846 GNAS 20 57484420 SNP C A Missense_Mutation p. R844S c.2530C > A NM_016592 0.080645 5 57 id16703 GNAS 20 57484421 SNP G A Missense_Mutation p.R844H c.2531G > A NM_016592 0.066667 5 70 id6830 GNAS 20 57484421 SNP G A Missense_Mutation p.R844H c.2531G > A NM_016592 0.121951 5 36 id12269 GNAS 20 57484421 SNP G A Missense_Mutation p.R844H c.2531G > A NM_016592 0.054348 5 87 id4939 GNAS 20 57484421 SNP G A Missense_Mutation p.R844H c.2531G > A NM_016592 0.05 4 76 id5860 GNAS 20 57484421 SNP G A Missense_Mutation p.R844H c.2531G > A NM_016592 0.155556 7 38 id15544 GNB1 1 1747227 SNP C A Missense_Mutation p. K57N c.171G > T NM_002074 0.098039 5 46 id3168 GNB1 1 1747227 SNP C A Missense_Mutation p. K57N c.171G > T NM_002074 0.131579 5 33 id13397 GNB1 1 1747228 SNP T A Missense_Mutation p.K57M c.170A > T NM_002074 0.096154 5 47 id12470 GNB1 1 1747228 SNP T A Missense_Mutation p.K57M c.170A > T NM_002074 0.083333 5 55 id4360 GNB1 1 1747229 SNP T C Missense_Mutation p.K57M c.170A > T NM_002074 0.211538 11 41 id8456 GNB1 1 1747229 SNP T C Missense_Mutation p.K57E c.169A > G NM_002074 0.214286 9 3 id11645 GNB1 1 1747229 SNP T C Missense_Mutation p.K57E c.169A > G NM_002074 0.121212 4 29 id8807 GNB1 1 1747229 SNP T C Missense_Mutation p.K57E c.169A > G NM_002074 0.285714 10 25 id11639 GNB1 1 1747229 SNP T C Missense_Mutation p.K57E c.169A > G NM_002074 0.090909 3 30 id3437 GNB1 1 1747229 SNP T C Missense_Mutation p.K57E c.169A > G NM_002074 0.16 4 21 id3822 GNB1 1 1747229 SNP T C Missense_Mutation p.K57E c.169A > G NM_002074 0.088235 3 31 id8979 GNB1 1 1747229 SNP T C Missense_Mutation p.K57E c.169A > G NM_002074 0.321429 9 19 id5020 GNB1 1 1747229 SNP T C Missense_Mutation p.K57E c.169A > G NM_002074 0.065217 3 43 id4971 GNB1 1 1747229 SNP T C Missense_Mutation p.K57E c.169A > G NM_002074 0.384615 15 24 id10936 GNB1 1 1747229 SNP T C Missense_Mutation p.K57E c.169A > G NM_002074 0.4375 14 18 id8647 GNB1 1 1747229 SNP T C Missense_Mutation p.K57E c.169A > G NM_002074 0.081633 4 45 id4772 GNB1 1 1747229 SNP T C Missense_Mutation p.K57E c.169A > G NM_002074 0.068966 4 54 id15602 GNB1 1 1747229 SNP T C Missense_Mutation p.K57E c.169A > G NM_002074 0.075 3 37 id9339 GNB1 1 1747229 SNP T C Missense_Mutation p.K57E c.169A > G NM_002074 0.278689 17 44 id6556 GNB1 1 1747229 SNP T C Missense_Mutation p.K57E c.169A > G NM_002074 0.208333 10 38 id12008 GNB1 1 1747229 SNP T C Missense_Mutation p.K57E c.169A > G NM_002074 0.130435 6 40 id1659 GNB1 1 1747229 SNP T C Missense_Mutation p.K57E c.169A > G NM_002074 0.104167 5 43 id4207 HIST1H1C 6 26056332 SNP T A Nonsense_Mutation p.K109* c.326A > G NM_005319 0.055215 9 154 id4838 IDH2 15 90631934 SNP C T Missense_Mutaton p.R140Q c.419G > A NM_002168 0.094118 8 77 id12629 IDH2 15 90631934 SNP C T Missense_Mutaton p.R140Q c.419G > A NM_002168 0.043011 4 89 id11438 IDH2 15 90631934 SNP C T Missense_Mutaton p.R140Q c.419G > A NM_002168 0.126437 11 76 id562 IKZF1 7 50468196 SNP C A Nonsense_Mutation p.Y477* c.1431C > A NM_006060 0.071429 3 39 id2011 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.05814 5 81 id16145 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.217742 27 97 id11641 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.27193 31 83 id9951 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.082569 9 100 id9952 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.058824 8 128 id3449 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.225806 21 72 id2348 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.287356 25 62 id15971 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.109434 29 236 id6999 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.909091 140 14 id2189 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.146789 16 93 id5972 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.091954 8 79 id9592 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.041237 4 93 id12296 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.420561 45 62 id13636 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.040816 4 94 id9457 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.072289 12 154 id4774 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.888 111 14 id5826 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.089888 8 81 id16987 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.062992 8 119 id15181 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.252252 28 83 id11973 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.043956 4 87 id15486 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.071429 8 104 id16838 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.097015 13 121 id13371 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.048544 10 196 id1470 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.241935 30 94 id12573 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.073171 9 114 id6354 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.05618 5 84 id15417 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.048193 8 158 id1341 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.438462 57 73 id5286 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.090909 9 90 id3877 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.111111 9 72 id12878 JAK2 9 5073770 SNP G T Missense_Mutaton p.V617F c.1849G > T NM_004972 0.051095 7 130 id428 JAK3 19 17949108 SNP C T Missense_Mutaton p.M511I c.1533G > A NM_000215 0.225108 52 179 id1146 JARID2 6 15501273 INS — G Frame_Shift_Ins p.L664fs c.2081 2082insG NM_004973 0.06 6 90 id1308 KDM6A X 44879925 SNP C T Nonsense_Mutation p.R172* c.514C > T NM_021140 0.647059 55 30 id12922 KDM6A X 44942739 DEL C — Frame_Shift_Del p.P1107fs c.3319delC NM_021140 0.27 8 22 id3440 KIT 4 55594197 SNP C T Missense_Mutaton p.R634W c.1900C > T NM_000222 0.113636 10 78 id3248 KLHL6 3 183273150 SNP T A Missense_Mutaton p.R98W c.292A > T NM_130446 0.061728 5 76 id14518 KRAS 12 25378561 SNP G A Missense_Mutaton p.A146V c.437C > T NM_033360 0.083333 4 44 id15632 KRAS 12 25378647 SNP T G Missense_Mutaton p.K117N c.351A > C NM_033360 0.2625 21 59 id13666 KRAS 12 25398285 SNP C A Missense_Mutaton p.G12C c.34G > T NM_033360 0.125 3 21 id4833 LUC7L2 7 139083380 DEL C — Frame_Shift_Del p.D64fs c.192delC NM_016019 0.1 18 160 id7124 LUC7L2 7 139086898 SNP C T Nonsense_Mutation p.Q91* c.271C > T NM_016019 0.119048 5 37 id5952 LUC7L2 7 139097301 SNP C T Nonsense_Mutation p.R262* c.785G > A NM_016019 0.128788 17 115 id4691 MLL2 19 36213944 INS — G Frame_Shift_Ins p.R924fs c.2770_2771insG NM_003482 0.05 6 126 id12338 MLL2 12 49427936 INS — T Frame_Shift_Ins p.K3551fs c.10653_10654insA NM_003482 0.13 16 111 id7288 MLL2 12 49434301 SNP G A Nonsense_Mutation p.Q2418* c.7252C > T NM_003482 0.069767 3 40 id17045 MLL2 12 49444933 DEL G — Frame_Shift_Del p.R845fs c.2533delC NM_003482 0.03 7 215 id8776 MPL 1 43814993 DEL CTG — In_Frame_Del p.L513del c.1528 1530delCTG NM_005373 0.03 6 190 id15871 MPL 1 43815009 SNP G T Missense_Mutaton p.W515L c.1544G > T NM_005373 0.632653 62 36 id3439 MYD88 3 38182641 SNP T C Missense_Mutaton p.L273P c.818T > C NM_001172567 0.10989 10 81 id4362 MYD88 3 38182641 SNP T C Missense_Mutaton p.L273P c.818T > C NM_001172567 0.128205 10 68 id12559 NOTCH1 9 139400000 SNP C A Nonsense_Mutation p.E1450* c.4348G > A NM_017617 0.06383 3 44 id12729 NOTCH1 9 139413957 DEL T — Frame_Shift_Del p.N268fs c.803delA NM_017617 0.02 6 272 id966 NOTCH2 1 120467977 SNP C A Nonsense_Mutation p.E1488* c.4462G > T NM_024408 0.044944 12 255 id11553 NOTCH2 1 120611991 SNP C T Nonsense_Mutation p.W10* c.30G > A NM_024408 0.090909 3 30 id12422 NRAS 1 115256528 SNP T G Missense_Mutaton p.Q61H c.183A > C NM_002524 0.107914 15 124 id15579 NRAS 1 115256529 SNP T C Missense_Mutaton p.Q61R c.182A > G NM_002524 0.045198 8 169 id2857 NRAS 1 115256530 SNP G T Missense_Mutaton p.Q61K c.181C > A NM_002524 0.055276 11 188 id15859 NRAS 1 115256532 SNP C T Missense_Mutaton p.G60E c.179G > A NM_002524 0.049296 7 135 id7257 NRAS 1 115258744 SNP C T Missense_Mutaton p.G13D c.38D > A NM_002524 0.065421 14 200 id16047 NRAS 1 115258745 SNP C G Missense_Mutaton p.G13R c.37G > C NM_002524 0.057971 12 195 id5915 PDSS2 6 107655446 DEL G — Frame_Shift_Del p.N129fs c.387delC NM_020381 0.52 47 43 id4938 PHF6 X 133551280 SNP G T Nonsense_Mutation p.G306* c.916G > T NM_ 001015877 0.161765 11 57 id9810 PIK3CA 3 178916726 SNP G A Missense_Mutaton p.R38H c.113G > A NM_006218 0.39759 33 50 id9419 PRDM1 6 106553280 INS — C Frame_Shift_Ins p.L415fs c.1245_1246insC NM_001198 0.5 39 39 id2010 PRDM1 6 106553536 INS — C Frame_Shift_Ins p.A501fs c.1501_1502insC NM_001198 0.09 6 58 id6622 PRDM1 6 106553596 INS — C Frame_Shift_Ins p.T521fs c.1561_1562insC NM_001198 0.15 6 35 id6724 PRPF40B 12 50027850 SNP C T Nonsense_Mutation p.Q241* c.721C > T NM_001031698 0.22 11 39 id11569 PTPN11 12 112888172 SNP A G Missense_Mutaton p.Y63C c.188A > G NM_002834 0.040404 4 95 id15878 PTPN11 12 112888172 SNP A G Missense_Mutaton p.Y63C c.188A > G NM_002834 0.382609 44 71 id4772 PTPN11 12 112888189 SNP G C Missense_Mutaton p.E69Q c.205G > C NM_002834 0.20354 23 90 id9785 RAD21 8 117862942 INS — T Frame_Shift_Ins p.1512fs c.1534_1535insA NM_006265 0.08 7 76 id16143 RAD21 8 117863003 INS G A Nonsense_Mutation p.Q492* c.1474C > T NM_006265 0.04878 4 78 id1013 RAD21 8 17864908 DEL GGTCTTCTG — Frame_Shift_Del p.P398fs c.1192_1201delCC NM_006265 0.07 6 79 id8977 G AGAAGACC RAD21 8 117869618 DEL TAGGAGGTT — Frame_Shift_Del p.S189fs c.566 576delCTAA NM_006265 0.17 13 64 id16332 AG CCTCCTA RAD21 8 117875449 SNP C T Missense_Mutaton p.R650 c.194G > A NM_006265 0.234483 34 111 id3404 RAD21 8 117875476 SNP G C Nonsense_Mutation p.S56* c.167C > G NM_006265 0.254777 40 117 id12270 RIT1 1 155874194 INS — A Frame_Shift_Ins p.R112fs c.336_337insT NM_006912 0.07 7 96 id8573 RPS15 19 1440427 SNP C G Missense_Mutaton p.A135G c.404C > G NM_001018 0.277778 25 65 id3771 SETD2 3 47084145 INS — G Frame_Shift_Ins p.P2381fs c.7143_7144insC NM_014159 0.04 7 177 id7288 SETD2 3 47103730 DEL TTTAT — Frame_Shift_Del p.N2138fs c.6413_6417delAT NM_014159 0.05 18 356 id12181 AAA SETD2 3 47125328 INS — G Frame_Shift_Del p.Q2048fs c.6142_6143insC NM_014159 0.02 6 259 id1369 SETD2 3 47158225 SNP G A Nonsense_Mutation p.R1492* c.4474C > T NM_014159 0.142857 13 78 id7102 SETDB1 1 150933381 INS — C Frame_Shift_Ins p.H948fs c.2843_2844insC NM_001145415 0.03 6 193 id7288 SF1 11 64533489 INS — G Frame_Shift_Ins p.Q574fs c.1720_1721insC NM_004630 0.11 6 47 id6458 SF1 11 64536811 SNP C G Splice_Site c.e7 − 1 c.664_splice NM_004630 0.104167 5 43 id15592 SF3A1 22 30737857 INS — G Frame_Shift_Ins p.P298fs c.894_895insC NM_005877 0.05 7 143 id7140 SF3B1 2 198266489 SNP C T Missense_Mutaton p.E783K c.2347G > A NM_012433 0.075269 7 86 id4885 SF3B1 2 198266611 SNP C T Missense_Mutaton p.G742D c.2225G > A NM_012433 0.070175 4 53 id8807 SF3B1 2 198266834 SNP T C Missense_Mutaton p.K700E c.2098A > G NM_012433 0.213333 16 59 id16099 SF3B1 2 198266834 SNP T C Missense_Mutaton p.K700E c.2098A > G NM_012433 0.1 7 63 id16100 SF3B1 2 198266834 SNP T C Missense_Mutaton p.K700E c.2098A > G NM_012433 0.230769 12 40 id12421 SF3B1 2 198266834 SNP T C Missense_Mutaton p.K700E c.2098A > G NM_012433 0.058824 4 64 id3358 SF3B1 2 198266834 SNP T C Missense_Mutaton p.K700E c.2098A > G NM_012433 0.140187 15 92 id3345 SF3B1 2 198266834 SNP T C Missense_Mutaton p.K700E c.2098A > G NM_012433 0.070175 4 53 id14902 SF3B1 2 198266834 SNP T C Missense_Mutaton p.K700E c.2098A > G NM_012433 0.133333 10 65 id3195 SF3B1 2 198266834 SNP T C Missense_Mutaton p.K700E c.2098A > G NM_012433 0.075 6 74 id9423 SF3B1 2 198266834 SNP T C Missense_Mutaton p.K700E c.2098A > G NM_012433 0.113208 6 47 id15231 SF3B1 2 198266834 SNP T C Missense_Mutaton p.K700E c.2098A > G NM_012433 0.105263 6 51 id5722 SF3B1 2 198266834 SNP T C Missense_Mutaton p.K700E c.2098A > G NM_012433 0.071429 5 65 id1002 SF3B1 2 198267359 SNP C A Missense_Mutaton p.K666N c.1998G > C NM_012433 0.170543 22 107 id3460 SF3B1 2 198267359 SNP C A Missense_Mutaton p.K666N c.1998G > C NM_012433 0.056818 5 83 id16012 SF3B1 2 198267359 SNP C G Missense_Mutaton p.K666N c.1998G > C NM_012433 0.078947 3 35 id15991 SF3B1 2 198267359 SNP C G Missense_Mutaton p.K666N c.1998G > C NM_012433 0.051282 4 74 id13848 SF3B1 2 198267359 SNP C G Missense_Mutaton p.K666N c.1998G > C NM_012433 0.174603 11 52 id2084 SF3B1 2 198267359 SNP C G Missense_Mutaton p.K666N c.1998G > C NM_012433 0.348485 23 43 id13636 SF3B1 2 198267359 SNP C G Missense_Mutaton p.K666N c.1998G > C NM_012433 0.084507 6 65 id9421 SF3B1 2 198267359 SNP C G Missense_Mutaton p.K666N c.1998G > C NM_012433 0.061728 5 76 id5804 SF3B1 2 198267359 SNP C A Missense_Mutaton p.K666N c.1998G > C NM_012433 0.067669 9 124 id3092 SF3B1 2 198267360 SNP T C Missense_Mutaton p.K666R c.1997A > G NM_012433 0.477612 32 35 id12296 SF3B1 2 198267361 SNP T C Missense_Mutaton p.K666E c.1996A > G NM_012433 0.097826 9 83 id8760 SF3B1 2 198267361 SNP T G Missense_Mutation p.K666Q c.1996A > C NM_012433 0.321429 27 57 id13638 SF3B1 2 198267371 SNP G T Missense_Mutation p.H662Q c.1986C > A NM_012433 0.055556 5 85 id13868 SF3B1 2 198272802 SNP G A Missense_Mutation p.R387W c.1159C > T NM_012433 0.173469 17 81 id2250 SFRS2 17 74732959 SNP G A Missense_Mutation p.P95L c.284C > T NM_003016 0.27907 12 31 id16191 SFRS2 17 74732959 SNP G C Missense_Mutation p.P95R c.284C > G NM_003016 0.084746 5 54 id16184 SFRS2 17 74732959 SNP G T Missense_Mutation p.P95H c.284C > A NM_003016 0.084746 5 54 id3410 SFRS2 17 74732959 SNP G C Missense_Mutation p.P95R c.284C > G NM_003016 0.228571 8 27 id3332 SFRS2 17 74732959 SNP G T Missense_Mutation p.P95H c.284C > A NM_003016 0.083333 66 66 id3299 SFRS2 17 74732959 SNP G A Missense_Mutation p.P95L c.284C > T NM_003016 0.12766 6 41 id13556 SFRS2 17 74732959 SNP G C Missense_Mutation p.P95R c.284C > G NM_003016 0.196078 10 41 id3026 SFRS2 17 74732959 SNP G T Missense_Mutation p.P95H c.284C > A NM_003016 0.289474 11 27 id15181 SFRS2 17 74732959 SNP G T Missense_Mutation p.P95H c.284C > A NM_003016 0.122807 7 50 id3927 SFRS2 17 74732960 SNP G T Missense_Mutation p.P95T c.283C > A NM_003016 0.087719 5 52 id16192 SFRS2 17 74733113 SNP A G Missense_Mutation p.Y44H c.130T > C NM_003016 0.166667 4 20 id15776 SMC1A X 53409443 SNP C T Missense_Mutation p.R1090H c.3269G > A NM_006306 0.1 3 27 id8615 SMC3 10 112343707 SNP C T Nonsense_Mutation p.R360* c.1078C > T NM_005445 0.111111 65 40 id6729 STAG1 3 136078015 SNP G A Nonsense_Mutation p.R971* c.2911C > T NM_005862 0.476744 41 45 id16988 STAG2 X 123200061 SNP T A Nonsense_Mutation p.C711* c.21331 > A NM_006603 0.082353 7 78 id3061 STAT3 17 40474420 SNP C A Missense_Mutation p.D661Y c.1981G > T NM_139276 0.075 15 185 id6064 STAT3 17 40474420 SNP C A Missense_Mutation p.D661Y c.1981G > T NM_139276 0.035897 7 188 id3856 STAT3 17 40474420 SNP C A Missense_Mutation p.D661Y c.1981G > T NM_139276 0.0301 9 290 id11824 STAT3 17 40475058 SNP C G Missense_Mutation p.G618R c.1852G > C NM_139276 0.116279 10 76 id15379 SUZ12 17 30315461 DEL AG — Frame_Shift_Del p.S382fs c.1146_1147delAG NM_015355 0.09 7 67 id5830 TBL1XR1 3 176750884 SNP G A Nonsense_Mutation p.R431* c.1291C > T NM_324665 0.054945 5 86 id9755 TET1 10 70442645 SNP G A Missense_Mutation p.R1656H c.4967G > A NM_030625 0.040404 4 95 id15581 TET2 4 106155316 INS — A Frame_Shift_Ins p.R73fs c.217_218insA NM_001127208 0.22 23 80 id16185 TET2 4 106155353 DEL A — Frame_Shift_Del p.Y85fs c.254delA NM_001127208 0.21 13 50 id16184 TET2 4 106155403 DEL TC — Frame_Shift_Del p.S123fs c.367_368delTC NM_001127208 0.05 9 161 id6767 TET2 4 106155544 DEL G — Frame_Shift_Del p.E170fs c.508delG NM_001127208 0.13 12 83 id6962 TET2 4 106155585 DEL T — Frame_Shift_Del p.D162fs c.486delT NM_001127208 0.17 10 49 id14838 TET2 4 106155640 DEL AT — Frame_Shift_Del p.I181fs c.541_542delAT NM_001127208 0.18 12 53 id14877 TET2 4 106155761 INS — A Frame_Shift_Ins p.T221fs c.662_663insA NM_001127208 0.41 29 41 id6024 TET2 4 106155879 DEL G — Frame_Shift_Del p.L260fs c.780delG NM_001127208 0.39 17 27 id15531 TET2 4 106155921 DEL C — Frame_Shift_Del p.I274fs c.822delC NM_001127208 0.15 6 35 id16146 TET2 4 106156030 INS — T Frame_Shift_Ins p.L311fs c.931_932insT NM_001127208 0.29 9 22 id12755 TET2 4 106156120 SNP C T Nonsense_Mutation p.Q341* c.1021C > T NM_001127208 0.297297 11 26 id3816 TET2 4 106156246 SNP C T Nonsense_Mutation p.Q383* c.1147C > T NM_001127208 0.06383 6 8 id13812 TET2 4 106156304 SNP C G Nonsense_Mutation p.S423* c.12680 > G NM_001127208 0.242718 25 78 id12421 TET2 4 106156315 INS — T Frame_Shift_Ins p.L427fs c.1279_1280insT NM_001127208 0.05 10 210 id6395 TET2 4 106156358 DEL C — Frame_Shift_Del p.S420fs c.1259delC NM_001127208 0.06 8 117 id4591 TET2 4 106156371 DEL C — Frame_Shift_Del p.S424fs c.1272delC NM_001127208 0.42 23 32 id16100 TET2 4 106156421 INS — T Frame_Shift_Ins p.S462fs c.1385_1386insT NM_001127208 0.12 11 79 id6669 TET2 4 106156452 DEL A — Frame_Shift_Del p.I472fs c.1416delA NM_001127208 0.41 24 35 id9978 TET2 4 106156540 SNP C T Nonsense_Mutation p.Q481* c.1441C > T NM_001127208 0.105263 6 51 id8613 TET2 4 106156500 INS — GTTC Frame_Shift_Ins p.T518fs c.1554_1555insGTTC NM_001127208 0.07 11 139 id6946 TET2 4 106156625 SNP C G Nonsense_Mutation p.S530* c.1589C > G NM_001127208 0.234043 22 72 id9844 TET2 4 106156665 DEL T — Frame_Shift_Del p.S543fs c.1629delT NM_001127208 0.2 21 83 id8992 TET2 4 106156687 SNP C T Nonsense_Mutation p.Q530* c.1588C > T NM_001127208 0.090909 3 30 id13286 TET2 4 106156693 DEL T — Frame_Shift_Del p.L532fs c.1594delT NM_001127208 0.07 7 91 id5667 TET2 4 106156694 SNP T A Nonsense_Mutation p.L532* c.1594delT NM_001127208 0.137931 4 25 id13788 TET2 4 106156729 SNP C T Nonsense_Mutation p.R544* c.1630C > T NM_001127208 0.0897447 7 71 id9033 TET2 4 106156790 SNP G A Nonsense_Mutation p.W585* c.1754G > A NM_001127208 0.066667 7 98 id10707 TET2 4 106156820 DEL A — Frame_Shift_Del p.Q595fs c.1784delA NM_001127208 0.11 12 94 id9756 TET2 4 106156835 INS — A Frame_Shift_Ins pL579fs c.1736_1737insA NM_001127208 0.04 6 134 id5827 TET2 4 106156862 SNP C G Nonsense_Mutation p.S588* c.1763C > G NM_001127208 0.085714 9 96 id17116 TET2 4 106156862 SNP C G Nonsense_Mutation p.S588* c.1763C > G NM_001127208 0.070423 5 56 id13618 TET2 4 106156936 DEL G — Frame_Shift_Del p.G634fs c.1900delG NM_001127208 0.28 29 73 id6827 TET2 4 106157003 DEL A — Frame_Shift_Del p.Q656fs c.1967delA NM_001127208 0.2 7 28 id9231 TET2 4 106157106 DEL A — Frame_Shift_Del p.P690fs c.2070del.A NM_001127208 0.1 7 62 id7339 TET2 4 106157155 DEL AG — Frame_Shift_Del p.R686fs c.2056_2057delA NM_001127208 0.04 7 160 id5554 TET2 4 106157173 INS — A Frame_Shift_Ins p.E692fs c.2074_2075insA NM_001127208 0.15 18 103 id14876 TET2 4 106157212 SNP C T Nonsense_Mutation p.Q705* c.2113C > T NM_001127208 0.181818 14 63 id3142 TET2 4 106157236 DEL T — Frame_ Shift_ Del p.F713fs c.2137delT NM_001127208 0.06 6 88 id8785 TET2 4 106157299 SNP C T Nonsense_Mutation p.Q734* c.2201A > G NM_001127208 0.181818 14 63 id12570 TET2 4 106157306 SNP C A Nonsense_Mutation p.S757* c.2270C > A NM_001127208 0.148936 14 80 id10777 TET2 4 106157313 DEL C — Frame_Shift_Del p.L759fs c.2277delC NM_001127208 0.39 36 57 id12296 TET2 4 106157326 DEL C — Frame_Shift_Del p.Q764fs c.2290delC NM_001127208 0.24 26 81 id9503 TET2 4 106157349 DEL AAAG — Frame_ Shift_ Del p.1771fs c.2313_2316delAA NM_001127208 0.05 9 162 id6622 TET2 4 AG TET2 4 106157371 SNP C T Nonsense_Mutation p.Q779* c.2335C > G NM_001127208 0.068493 5 68 id11567 TET2 4 106157384 INS — C Frame_Shift_Ins p.H783fs c.2348_2349insC NM_001127208 0.08 8 95 id9593 TET2 4 106157407 SNP C T Nonsense_Mutation p.Q770* c.2308C > T NM_001127208 0.087912 8 83 id16702 TET2 4 106157493 INS — A Frame_Shift_Ins p.E819fs c.2457 2458insA NM_001127208 0.16 11 59 id6628 TET2 4 106157615 DEL A — Frame_ Shift_ Del p.H839fs c.2516delA NM_001127208 0.16 8 43 id5794 TET2 4 106157616 DEL C — Frame_ Shift_ Del p.H839fs c.2516delA NM_001127208 0.11 11 90 id6042 TET2 4 106157671 DEL A — Frame_ Shift_ Del p.K858fs c.2572delA NM_001127208 0.18 12 50 id15401 TET2 4 106157695 SNP C T Nonsense_Mutation p.Q866* c.2596C > T NM_001127208 0.207547 11 42 id16170 TET2 4 106157731 DEL C — Frame_ Shift_ Del p1818fs c.2632delC NM_001127208 0.29 14 34 id7611 TET2 4 106157816 INS — GTCTG Frame_Shift_Ins p.M906fs c.2717_2718insGTC NM_001127208 0.08 6 68 id3168 TET2 4 TG TET2 4 106157824 SNP C T Nonsense_Mutation p.Q909* c.2725C > T NM_001127208 0.113208 6 47 id15069 TET2 4 106157827 SNP C T Nonsense_Mutation p.Q910* c.2728C > T NM_001127208 0.142857 7 42 id3120 TET2 4 106157833 DEL GC — Frame_ Shift_ Del p.A912fs c.2734_2735delGC NM_001127208 0.16 9 49 id14836 TET2 4 106157845 SNP C T Nonsense_Mutation p.Q937* c.2809C > T NM_001127208 0.068182 3 41 id9874 TET2 4 106157845 SNP C T Nonsense_Mutation p.Q916* c.2746C > T NM_001127208 0.104478 7 60 id14980 TET2 4 106158065 INS — A Frame_Shift_Ins p.P989fs c.2966_2967insA NM_001127208 0.16 10 53 id8791 TET2 4 106158140 INS — A Frame_Shift_Ins p.A1014fs c.3041_3042insA NM_001127208 0.28 17 44 id2276 TET2 4 106158160 SNP C T Nonsense_Mutation p.Q1042* c.3124C > T NM_001127208 0.048951 7 136 id6161 TET2 4 106158187 SNP C T Nonsense_Mutation p.Q1030* c.3088C > T NM_001127208 0.396825 25 38 id2189 TET2 4 106158226 INS — AT Frame_Shift_Ins p.H1064fs c.3190_3191insAT NM_001127208 0.06 6 88 id10415 TET2 4 106158332 DEL C — Frame_Shift_Del p.T1078fs c.3233deIC NM_001127208 0.05 6 124 id8806 TET2 4 106158356 DEL CT — Frame_Shift_Del p.11107fs c.3320_3321delCT NM_001127208 0.08 8 95 id2308 TET2 4 106158408 DEL TT — Frame_Shift_Del p.N1103fs c.3309_3310delTT NM_001127208 0.05 6 105 id14667 TET2 4 106158408 DEL T — Frame_Shift_Del p.N1103fs c.3309_3310delTT NM_001127208 0.22 23 82 id12573 TET2 4 106158439 DEL AC — Frame_Shift_Del p.T1114fs c.3340_3341delAC NM_001127208 0.08 6 69 id15950 TET2 4 106158479 INS — T Frame_Shift_Del p.Q1127fs c.3380_3381insT NM_001127208 0.33 27 55 id6026 TET2 4 106158503 SNP G A Missense_Mutation p.C1135Y #N/A NM_001127208 0.278689 17 44 id9984 TET2 4 106158509 SNP G A Splice_Site c.e3 + 1 c.3472_splice NM_001127208 0.3 27 63 id11641 TET2 4 106158509 SNP G A Splice_Site c.e3 + 1 c.3472_splice NM_001127208 0.153153 17 94 id10803 TNFAIP3 6 138196014 SNP C T Nonsense_Mutation p.Q110* c.328C > T NM_006290 0.0625 4 60 id5021 TNFAIP3 6 138196885 SNP C T Nonsense_Mutation p.R183* c.518G > A NM_006290 0.09375 9 87 id16445 TNFAIP3 6 138198296 SNP G T Nonsense_Mutation p.E297* c.889G > T NM_006290 0.177419 11 51 id15210 TNFRSF14 1 2489781 SNP G C Splice_Site c.e3 -1 c.179_splice NM_003820 0.102941 7 61 id14548 TNFRSF14 1 2491327 DEL G — Frame_Shift_Del p.G124fs c.370delG NM_003820 0.39 7 11 id2761 TP53 17 7574003 SNP G A Nonsense_Mutation p.R342* c.1024C > T NM_001126112 0.067797 4 55 id3959 TP53 17 7577090 SNP C T Missense_Mutation p.R283H c.848G > A NM_001126112 0.421053 24 33 id9876 TP53 17 7577094 SNP C T Missense_Mutation p.R282W c.844C > T NM_001126112 0.071429 3 39 id16017 TP53 17 7577097 SNP C T Missense_Mutation p.D281N c.841G > A NM_001126112 0.327586 19 39 id2305 TP53 17 7577100 SNP T C Missense_Mutation p.R280G c.838A > G NM_001126112 0.087719 5 52 id16933 TP53 17 7577538 SNP C T Missense_Mutation p.R248Q c.743G > A NM_001126112 0.537736 57 49 id4884 TP53 17 7577538 SNP C T Missense_Mutation p.R248Q c.743G > A NM_001126112 0.164706 14 71 id4005 TP53 17 7577548 SNP C A Missense_Mutation p.G245C c.733G > T NM_001126112 0.056338 8 134 id6889 TP53 17 7577559 SNP G C Missense_Mutation p.S241C c.722C > G NM_001126112 0.1875 9 8 id17155 TP53 17 7577578 SNP T C Missense_Mutation p.N235D c.703A > G NM_001126112 0.081633 4 45 id14981 TP53 17 7578190 SNP T C Missense_Mutation p.Y220C c.659A > G NM_001126112 0.085106 4 43 id5608 TP53 17 7578191 SNP A G Missense_Mutation p.Y220H c.658T > C NM_001126112 0.139535 6 37 id3142 TP53 17 7578271 SNP T C Missense_Mutation p.H193R c.578A > G NM_001126112 0.0625 3 45 id6883 TP53 17 7578389 SNP G A Missense_Mutation p.R181C c.541C > T NM_001126112 0.080645 5 57 id14694 TP53 17 7578400 SNP G C Missense_Mutation p.P177R c.530C > G NM_001126112 0.193548 12 50 id5804 TP53 17 7578406 SNP C T Missense_Mutation p.R175H c.524G > A NM_001126112 0.047619 4 80 id9682 TP53 17 7578406 SNP C T Missense_Mutation p.R175H c.524G > A NM_001126112 0.186047 8 35 id15836 TP53 17 7578406 SNP C T Missense_Mutation p.R175H c.524G > A NM_001126112 0.048544 5 98 id9552 TP53 17 7578412 SNP A T Missense_Mutation p.V173E c.518T > A NM_001126112 0.089888 8 81 id10986 TP53 17 7578457 SNP C T Missense_Mutation p.R158H c.473G > A NM_001126112 0.082474 8 89 id4972 TP53 17 7578463 SNP C T Missense_Mutation p.R156H c.467G > A NM_001126112 0.041237 4 93 id13772 TP53 17 7578475 SNP G A Missense_Mutation p.P152L c.455C > T NM_001126112 0.127273 7 48 id17168 TP53 17 7578492 SNP C T Nonsense_Mutation p.W146* c.438G > A NM_001126112 0.092308 6 59 id13823 TP53 17 7578493 SNP C T Nonsense_Mutation p.W146* c.438G > A NM_001126112 0.146341 6 35 id16840 TP53 17 7578536 SNP T C Missense_Mutation p.K132E c.394A > G NM_001126112 0.065217 3 43 id15972 TP53 17 7578536 SNP T C Missense_Mutation p.K132E c.394A > G NM_001126112 0.219512 9 32 id15950 TP53 17 7579315 INS — C Frame_Shift_Ins p.C124fs c.371_372insG NM_001126112 0.09 13 133 id14877 TP53 17 7579358 SNP C A Missense_Mutation p.R110L c.329G > T NM_001126112 0.104762 11 94 id14122 TP53 17 7579359 SNP G A Missense_Mutation p.R110C c.328C > T NM_001126112 0.207547 11 42 id16019 TP53 17 7579359 SNP G A Missense_Mutation p.R110C c.328C > T NM_001126112 0.439394 29 37 id16285 TP53 17 7579470 INS — G Frame_Shift_Ins p.P72fs c.216delC NM_001126112 0.03 14 418 id7283 TP53 17 7579471 DEL G — Frarne_Shift_Del p.P72fs c.216delC NM_001126112 0.17 31 149 id15592 U2AF1 21 44514780 SNP C T Missense_Mutation p.R156H c.467G > A NM_006758 0.329897 32 65 id2344 U2AF1 21 44514780 SNP C T Missense_Mutation p.R156H c.467G > A NM_006758 0.475 38 42 id6999 U2AF1 21 44524456 SNP G A Missense_Mutation p.101C > T c.101C > T NM_006758 0.12963 7 47 id11641 U2AF1 21 44524456 SNP G A Missense_Mutation p.S34F c.101C > T NM_006758 0.190476 4 17 id4102 U2AF1 21 44527564 SNP T C Missense_Mutation p.D14G c.41A > G NM_006758 0.068966 6 81 id15962 ZRSR2 X 15822298 SNP G C Missense_Mutation p.R126P c.377G > C NM_005089 0.75 15 5 id8081 ZRSR2 X 15838385 SNP C T Nonsense_Mutation p.R295* c.883C > T NM_005089 0.443038 35 44 id13539 ZRSR2 X 15841188 DEL A — Frame_Shift_Del p.G424fs c.1272delA NM_005089 0.58 11 8 id7280

SUPPLEMENTARY TABLE 4 List of non-hematopoietic genes and variants queried Number of Gene variants name Reported mutations used for variant calling Accession found ACVR1B Frameshift/nonsense, G368spl, R420spl, G219E, G219W, D268N, M345I, I346F, A357T, NM_004302 0 H358Y, H374R, D376N, A446V, D490N, D490V AKT1 E17K, L52R, Q79K, W80R NM_005163 0 APC Frameshift/nonsense c150-1500, R141spl, R216spl, R2204* NM_001127511 1 APOL2 S127L, S127C, S128D NM_030882 0 ARH6AP35 Frameshift/ns NM_004491 0 ARID2 Frameshift/ns, D174Y, D174G, R247H, R247S, R285W, R285Q NM_152641 0 ATP5B S386L, K459T NM_001686 0 B2M Frameshift/ns NM_004048 0 BAP1 Frameshift/nonsense, G23spl, D311spl, N78S, R227H, R227C NM_004656 0 CASP8 Frameshift/ns NM_033355 4 CDH1 Frameshift/ns, E243K, E243Q, R732Q, P825L NM_004360 0 CDKN1B Frameshift/ns NM_004064 0 CDKN2A Q50*, V51spl, R58*, G65spl, D74V, D74Y, R80*, H83R, H83Y, D84N, D84V, E88*, D108V, NM_000077 0 D108N, D108Y, W110*, P114L, P114T, E120*, Y129*, D153spl CHD1 M1I, I1080spl, R1189Q NM_001270 0 CHD4 E34spl, P251L, P251S, R975H, R1105W, R1105Q, R1162W, R1162Q, R1338I, R877Q, NM_001273 0 R877W CTNNB1 D32Y, D32A, D32G, D32N, S33C, S33P, S33F, S33Y, G34R, G34E, G34V, I35S, H36Y, S37F, NM_001904 0 S37C, S37A, T41A, T41I, S45F, S45C, S45Y EGFR L62R, G63R, R108K, R108G, S220P, R222C, R222L, R252C, R252P, A289V, A289T, H304Y, NM_005228 0 P596L, P596S, G598V, E709K, G719A, G719D, S768I, S768T, L838M, L858R, L861Q, L861R ERBB2 S310F, S310Y, R678Q, L755S, L755R, D769H, D769N, D769Y, V777L, V842I, R896H, NM_004448 0 M916I, E1244D, E1244Q ERBB3 M91I, V104L, V104M, D297N, D297Y, D297V, K329E, K329T, T355A, T355I, E928G, S1216F NM_001982 0 EZH1 Frameshift/ns NM_001991 0 FGFR3 R248C, S249C, G380R, K650T, K650E, P716H NM_000142 0 GPS2 Frameshift/ns NM_004489 1 HRAS G12A, G12D, G12S, G13R, G13V, G13C, Q61R, Q61L, Q61K NM_176795 0 KDM5C Frameshift/ns, E23K, E23Q, E386Q, R585C, R585H, C730F, C730R, E1247K NM_004187 0 KEAP1 S144F, I145F, V155A, V155F, R169C, R260L, R260Q, R272C, R272H, R320P, R320Q, R320M, NM_012289 0 R415H, R415C, G417E, G417V, G417R, D422N, S423V, R470S, R470H, R470C, G480W, E493V, E493D, M503I, M503K, W544R, W544C, G603W MAP2K4 Frameshift/nonsense, I73spl, R134W, S184L, S251I, P272spl, L297spl, R287H, P306R, P306H, NM_003010 0 L362spl, K363spl, L385P, L385V MAP3K1 Frameshift/ns, S1330L, S1330W, C1437S, C1437F NM_005921 1 MTOR L1460P, C1483R, C1483Y, C1483F, E1799K, F1888V, F1888I, F1888L, I1973F, T1977K, T1977I, NM_004958 0 T1977S, V2006L, V2006I, S2215Y NFE2L2 W24C, W24R, Q26R, Q26K, D27G, D27H, D29N, D29H, D29Y, L30F, L30H, G31A, G31V, G31R, NM_006164 1 R34P, R34G, R34Q, E79K, E79Q, T80P, T80R, T80K, G81V, G81S, G81C, G81D, E82D, A124G, A124V PBRM1 Frameshift/ns NM_181042 0 PIK3CA R38C, R38L, R38H, R38S, E39K, E81K, R88Q, C90Y, C90G, R93W, R93Q, G106V, G106R, R108L, NM_006218 0 R108H, E110del, K111R, K111E, K111N, K111del, L113del, R115L, G118spl, V344G, V344A, V344M, N345K, D350G, D350N, G364R, E365V, E365K, C420R, C420G, G451V, G451R, E453K, E453Q, E453D, P471A, P471L, E542K, E542Q, E545K, E545A, E545Q, E545D, E545G, Q546R, Q546P, Q546K, E726K, C901F, D939G, M1004I, G1007R, Y1021C, Y1021H, T1025A, T1025S, M1043V, M1043I, N1044K, N1044Y, H1047R, H1047L, G1049R PIK3R1 Frameshift/nonsense, P129L, P129S, R340spl, G376R, Y452C, Y452N, Y452H, D464N, D464Y, NM_181523 1 R503Q, R514C, R514L, D560H, D560G, D560N, L573P, Y580C, Y580D, M582spl RASA1 Frameshift/nonsense, S122L, S418spl, R789L, R789Q, M802I, P868spl NM_002890 0 RB1 Frameshift/nonsense, N405spl NM_000321 0 SEPT12 G56spl, R210spl, I211spl, R242spl, D243spl, R244*, NM_144605 0 SMAD4 Frameshift/ns, R97H, D351H, P356R, P356L, R361H, R361C, G386D, G510E, G510R, D537G, NM_005359 0 D537Y, D537V, P544S, P544L SMARCA4 P12L, P12S, R381Q, A406T, T786N, T786I, T910M, P913L, P913Q, R966W, R1125G, R1125S, NM_003072 0 R1125M, R1192C, R1192H, Q1195K, Q1195L, A1231D, A1231T, G1232C, G1232S SPOP E50K, M117V, S119N, S119R, R121Q, W131G, W131C, F133C, F133L, F133S, D140N, D140H NM_001007228 0 STK11 Frameshift/nonsensesplice-site NM_000455 0 VHL Frameshift/nonsensesplice-site, N78S, N78D, N78Y, W88R, W88L, L89P, L89H, S111R, S111H, NM_000551 1 H115N, V130L, V130D, I151N, I151T, I151F, L158P, L158V, C162R, C162F, C162Y, L169P, L184P, L188P, L188R Total 10

SUPPLEMENTARY TABLE S5 Called variants in non-hematopoeitic genes Gene Start Variant Reference Variant Protein cDNA name Chromosome position Type Allele Allele Variant Classification Change Change APC 5 112175212 DEL AAAAG — Frame_Shift_Del p.I1307fs c.3921_3925 CASP8 2 202149654 DEL C — Frame_Shift_Del p.N306fs c.918delC CASP8 2 202149654 DEL C — Frame_Shift_Del p.N306fs c.918delC CASP8 2 202149901 SNP C T Nonsense_Mutation p.Q389* c.1165C>T CASP8 2 202149901 SNP C T Nonsense_Mutation p.Q389* c.1165C>T GPS2 17 7217689 SNP G G Nonsense_Mutation p.Q80* c.238C>T MAP3K1 5 56152535 SNP G G Nonsense_Mutation p.W197* c.591G>A NFE2L2 2 178090009 SNP G A Nonsense_Mutation p.A124V c.371C>T PIK3R1 5 67589169 SNP C C Nonsense_Mutation p.R386* c.1156C>T VHL 3 10191614 SNP C T Nonsense_Mutation p.Q203* c.6075C>T Variant Variant Reference Gene Start allele allele allele name Chromosome position Accession fraction amount count ID APC 5 112175212 NM_001127511 0.28 29 74 id3447 CASP8 2 202149654 NM_033355 0.51 94 89 id8185 CASP8 2 202149654 NM_033355 0.47 95 109 id7570 CASP8 2 202149901 NM_033355 0.387755 19 30 id16144 CASP8 2 202149901 NM_033355 0.385714 27 43 id4204 GPS2 17 7217689 NM_004489 0.098361 12 110 id11021 MAP3K1 5 56152535 NM_005921 0.422414 49 67 id6400 NFE2L2 2 178090009 NM_006164 0.069767 3 40 id14589 PIK3R1 5 67589169 NM_181523 0.107143 3 25 id11720 VHL 3 10191614 NM_000551 0.333333 13 26 id13940

SUPPLEMENTARY TABLE S6 Logistic regression for factors associated with clonality Logistic regression was performed using the variables age (as a continuous variable), ancestry, sex, T2D, and age/sex interaction. Other interaction terms were modeled, but none were significant. Proportion of variance explained is derived by analysis of variance (ANOVA) for the generalized linear model, and is equal to deviance for the variable divided by residual deviance for the null model. Beta Variance coefficient OR(95 CI) p-value explained Age 0.07 1.08(1.07-1.09) <0.001 0.06 European (referent) 0.003 African-American 0.11 1.12(0.9-1.39)  0.3 East-Asian 0.02 1.02(0.79-1.31) 0.86 Hispanic −0.39 0.68(0.55-0.83) <0.001 South Asian −0.2 0.82(0.63-1.05) 0.125 No T2D (referent) 0.002 Has T2D 0.26  1.3(1.12-1.51) <0.001 Male (referent) 0.001 Female 0.83 0.066 Age:Female −0.02 0.98(0.97-1)   0.023 0.001

SUPPLEMENTARY TABLE S7 Logistic regression for factors associated with clonality by ancestry group Logistic regression was performed using the variables age (as a continuous variable), sex, T2D, and age/sex interaction for each ancestry group. Proportion of variance explained is derived by analysis of variance (ANOVA) for the generalized linear model, and is equal to deviance for the variable divided by residual deviance for the null model. Beta Variance coefficient OR(95 CI) p-value explained African-American Age 0.07 1.08(1.05-1.1)  <0.001 0.08 Female 0.37 1.45(0.21-10.2) 0.7 0.002 Has T2D 0.24 1.27(0.9-1.79)  0.18 0.002 Age:Sex −0.01 0.99(0.96-1.02) 0.52 0.0003 East Asian Age 0.1 1.11(1.06-1.16) <0.001 0.03 Female 1.3 3.65(0.08-172)  0.51 0.003 Has T2D −0.12 0.89(0.57-1.38) 0.61 0.0003 Age:Sex −0.03 0.97(0.32-1.03) 0.385 0.0006 European Age 0.07 1.07(1.06-1.09) <0.001 0.05 Female 1.73 5.62(1.26-25)  0.023 0.001 Has T2D 0.31 1.36(1.04-1.79) 0.026 0.003 Age:Sex −0.03 0.97(0.95-0.99) 0.013 0.0033 Hispanic Age 0.08 1.08(1.06-1.11) <0.001 0.08 Female 0.69   2(0.27-15.6) 0.5 0.00001 Has T2D 0.22 1.24(0.9-1.72)  0.18 0.001 Age:Sex −0.01 0.99(0.96-1.02) 0.485 0.0003 South Asian Age 0.08 1.08(1.05-1.11) <0.001 0.07 Female −0.82 0.44(0.01-12.3) 0.64 0.0001 Has T2D 0.49 1.63(1.04-2.56) 0.033 0.007 Age:Sex 0.01 1.01(0.96-1.07) 0.677 0.0002

SUPPLEMENTARY TABLE S8 Logistic regression for factors associated with RDW ≥ 14.5% Individuals were from Jackson Heart Study or UA control cohort. Covariate OR (95% CI) p-value Age 60-69 1.3(0.9-1.8) 0.17 Age 70-79 1.8(1.2-2.7) 0.002 Age 80-89 2.7(1.3-5.2) 0.005 Age >90 1.7(0.9-3.1) 0.09 Female 1.1(0.8-1.5) 0.76 Has T2D 0.9(0.7-1.2) 0.45 DNMT3a 1.9(1-3.6)  0.048 mutation WBC 1.1(1.0-1.1) 0.092 Hemoglobin 0.7(0.7-0.8) <0.001 Platelet count 1.0(1.0-1.0) 0.65

SUPPLEMENTARY TABLE S9 Association of cytopenias with clonality Individuals were classified as having cytopenia as defined in Methods. Statistical comparisons were performed using Fisher's exact test. WBC - white blood cell count, Hgb - hemoglobin, Plt - platelet count. Individuals were from Jackson Heart Study, Longevity Genes Project, Botnia, Helsinki-sib, and Malmo-sib. Of the 5 individuals with multiple cytopenias and clonal mutations, 2 had 2 detectable mutations, suggesting that these might be undiagnosed cases of MDS. Clone No Clone Low WBC 6 59 65 Normal WBC 119 2628 2747 125 2687 OR 2.2(0.8-5.2), p = 0.066 Low Hgb 30 616 646 Normal Hgb 109 2350 2459 139 2966 OR 1.0(0.7-1.6), p = 0.83 Low Plt 4 107 111 Normal Plt 132 2789 2921 136 2896 OR 0.8(0.2-2.1), p = 0.82 Any cytopenia 35 745 780 No cytopenia 104 2224 2328 139 2969 OR 1.0(0.6-1.5), p = 1 2+ cytopenias 5 37 42 1 or 0 134 2932 3066 cytopenias 139 2969 OR 3.0(0.9-7.7), p = 0.037 Lower limit of normal for blood counts were defined as follows: White blood cell count: African-Americans 3.0 × 10⁹/L, white 4.0 × 10⁹/L Hemoglobin: African-American women 11.2 g/dL, African-American men 12.5 g/dL, white women 11.9 g/dL, white men 13.4 g/dL Platelet count: 150 × 10⁹ L

SUPPLEMENTARY TABLE S10 Association of clonality with known versus unknown causes of anemia During clinical evaluation, most patients with anemia can be found to have an attributable cause. Using subjects from the Jackson Heart Study, Applicants assessed whether the anemia was attributable to iron deficiency, anemia of chronic inflammation, or renal insuffuciency. Individuals with clonality were less likely to have anemia attributable to one of these causes. Iron deficiency anemia/microcytic anemia mean corpuscular volume <80 fL ferritin <20 ng/mL ferritin 20-100 ng/mL WITH EITHER total iron binding capacity >370 mcg/dL OR serum iron <50 mcg/dL OR iron saturation <20% Anemia of chronic disease serum iron <65 mcg/dL WITH total iron binding capacity <250 mcg/dL ferritin >350 ng/mL for males ferritin >300 ng/mL for females Renal insufficiency estimated glomerular filtration rate <30 mL/min/1.73 m² Clone No Clone Known cause 2 360 362 Unknown cause 7 151 158 9 511 OR 0.1(0-0.6), p = 0.004

SUPPLEMENTARY TABLE S11 Details on subjects that developed hematologic malignancies Incident Cases Age at Co- Adjudi- Latency Mutation on Mutations on sampling Diagnosis hort cated (years) WES (VAF) RHP (VAF) WBC HGB PLT Death Cause of death 77 CANCER OF AJ No 6 JAK2 NA 7.8 11 247 Yes CARCINOMA SPLEEN_(a) p.V617F UNSPECIFIED (0.23) SITE 64 LEUKEMIA AJ No 7 ASXL1 ASXL1 3.5 12.9 189 No [prior NHL]_(b) p.D516fs p.D516fs (0.23) (0.18) 57 LYMPHOMA_(c) AJ No 2 DNMT3A NA 14.3 (51.3% 11 248 No p.R082H lymphocytes)_(d) (0.29) 85 DLBCL, large MEC Yes 5 TET2 TET2 No intestine_(e) p.C1135Y p.C1135Y (0.28)/A5XL1 (0.35)/TET2 p.1919fs p.G1192V (0.22) (0.30)/ASXL1 p.1919fs (0.26) 82 MDS-RAEB MEC Yes 7 ASXL1 ASXL1 Yes Myeloid p.T514fs p.T514fs leukemia (0.24) (0.30)/TET2 p.1616fs (0.15) 50 LYMPHOMA AJ No 4 None NA 6.3 14.4 220 Yes COMPLI- CATIONS DUE TO LYMPHOMA 48 LYMPHOMA AJ No 1 None NA 4.6 14.3 225 Yes RENAL FAILURE 57 LYMPHOMA AJ No 7 None None 7.4 13.7 280 Yes HYPOXIC RESPIRATORY FAILURE 62 LEUKEMIA AJ No 9 None NA 2.6 13.9 160 No 51 BLOOD AJ No 8 None NA 5.7 13.7 318 No 67 LEUKEMIA AJ No 9 None None No 64 MULTIPLE AJ No 9 None NA 4.4 11.4 298 No MYELOMA 59 LYMPHOMA AJ No 8 None NA 13.2 195 No 61 ACUTE AJ No 10 None None 11.7 158 Yes ACUTE MYELOID MYELOID LEUKEMIA LEUKEMIA 51 LEUKEMIA AJ No 10 None NA 4.0 11 334 No 66 Multiple MEC Yes 8 None NA No myeloma Prevalent Cases Time Age at Co- Adjudi- (years Mutation on Mutations on sampling Diagnosis hort cated prior) WES (VAF) RHP (VAF) WBC HGB PLT Death Cause of death 64 LEUKEMIA AJ No 7 None NA 3.5 12.9 189 No 48 LYMPHOMA AJ No 1 None NA 4.6 14.3 225 Yes RENAL FAILURE 76 LEUKEMIA AJ No 12 None NA 11.4 252 No 63 NHL oropharynx MEC Yes 24 None NA 64 NHL (subsequent AJ No 7 ASXL1 ASXL1 3.5 12.9 189 No LEUKEMIA, see p.D516fs p.D516fs above) (0.23) (0.18) NHL = Non-Hodkin's lymphoma, DLBCL = diffuse large B-cell lymhoma, MDS-RAEB = myelodysplastic syndrome, refractory anemia with excess blasts AJ = Jackson Heart Study, MEC = Multi-ethnic cohort, Hispanics in Los Angeles WES = whole exome sequencing, RHP = Rapid Heme Panel targeted re-sequencing. VAF = variant allele fraction WBC = white blood cell count (×10{circumflex over ( )}9 cells/L), HGB = hemoglobin (g/dL), PLT = platetet count (×10{circumflex over ( )}9 cells/L) NA = Not available _(a)This is likely splenomegaly secondary to a JAK2-mutated myeloproliferative neoplasm _(b)This likely represents a therapy related AML/MDS, as ASXL1 mutations have never been described in lymphoid malignancies _(c)DNMT3A mutations have been found in peripheral T-cell lymphomas and early T-progenitor ALL _(d)This is the second highest absolute lymphocyte count in the cohort of 2,426 subjects who had a WBC differential. The subject with the highest absolute lymphocyte count also had a DNMT3A mutation. _(e)TET2 has been reported to be mutated in 6-12% of DLBCL, and the mutations are reported to be found in hematopoietic stem cells

SUPPLEMENTARY TABLE S12 Risks associated with developing incident coronary heart disease and ischemic stroke using traditional risk factors and clonality. Model 1 Model 2 Model 3 Covariate HR (95% CI) p-value HR (95% CI) p-value HR (95% CI) p-value A n = 2,286 log(Age)  5.3 (2.1-12.8) <0.001 4.7 (1.9-11)  <0.001 4.6 (1.9-11)  <0.001 Has T2D 3.3 (2.1-5.3) <0.001 3.4 (2.1-5.4) <0.001 3.5 (2.2-5.5) 0.001 Female 0.7 (0.5-1.1) 0.11 0.7 (0.5-1.1) 0.13 0.7 (0.5-1.1) 0.13 HDL < 35 1.1 (0.6-2.1) 0.77 1.1 (0.6-2.1) 0.81 1.1 (0.6-2.1) 0.78 mg/dL HDL > 60 0.7 (0.4-1.2) 0.18 0.7 (0.4-1.3) 0.25 0.7 (0.4-1.3) 0.26 mg/dL TC > 240 2.1 (1.3-3.2) <0.001  2 (1.3-3.1) <0.001  2 (1.3-3.1) <0.001 mg/dL Former or 1.6 (1.1-2.5) 0.024 1.6 (1-2.4)  0.035 1.6 (1.1-2.5) 0.02 current smoker Hypertension 1.6 (1-2.5)  0.06 1.4 (0.9-2.3) 0.15 1.4 (0.9-2.3) 0.15 stage II-IV BMI > 25 1.2 (0.6-2.5) 0.55 |1.4 (0.6-2.8)  0.43 1.3 (0.6-2.8) 0.42 Clone present 2.3 (1.1-4.8) 0.026 VAF < 0.10 1.4 (0.5-4)  0.55 VAF < 0.10  4.4 (1.9-10.5) <0.001 Pseudo Log- −661 −658 −656 likelihood Pseudo likelihood 103 on 9 df 109 on 10 df 113 on 11 df ratio test B n = 2,420 log(Age) 14.6 (4.8-44.7) <0.001 13.3 (4.3-40)  <0.001 13.1 (4.3-40)  <0.001 Has T2D 2.9 (1.7-5)  <0.001 2.9 (1.7-5)  <0.001  3 (1.7-5.2) <0.001 Female 0.8 (0.5-1.3) 0.44 0.9 (0.5-1.4) 0.53 0.9 (0.5-1.4) 0.55 HDL < 35 1.2 (0.6-2.4) 0.52 1.3 (0.7-2.5) 0.45 1.3 (0.7-2.5) 0.45 mg/dL HDL > 60  1 (0.6-1.9) 0.95 1.1 (0.6-2)  0.84 1.1 (0.6-2)  0.86 mg/dL TC > 240 1.3 (0.8-2.1) 0.29 1.3 (0.8-2.1) 0.31 1.3 (0.8-2.1) 0.29 mg/dL Former or 1.8 (1.1-2.9) 0.014 1.8 (1.1-2.9) 0.016 1.8 (1.1-2.9) 0.014 current smoker Hypertension 1.8 (1-3.1)  0.037 1.7 (0.9-2.9) 0.077 1.7 (1-2.9)  0.074 stage II-IV BMI > 25 1.5 (0.6-3.6) 0.35 1.6 (0.7-4)  0.3 1.6 (0.7-3.9) 0.3 Clone present 2.2 (1.1-4.6) 0.029 VAF < 0.10 1.8 (0.7-4.6) 0.2 VAF > 0.10 3.1 (1.2-8.4) 0.025 Pseudo Log- −464 −462 −462 likelihood Pseudo likelihood 79 on 9 df 83 on 10 df 83.5 on 11 df ratio test Hazard ratios were estimated using competing risks regression with death as the competing risk. P-values are derived from the Fine-Gray test. Individuals with prior coronary heart disease (CHD) were excluded for CHD analysis, and individuals with prior ischemic stroke were excluded for stroke analysis. Models shown in A or B are from the same population and differ by having covariates either removed or added. A) Coronary heart disease, B) ischemic stroke. Individuals were from Jackson Heart Study and FUSION.

SUPPLEMENTARY TABLE S13 Risks associated with developing incident coronary heart disease using traditional risk factors as well as hsCRP, RDW, and clonality. Hazard ratios were estimated using competing risks regression with death as the competing risk. P-values are derived from the Fine- Gray test. Individuals with prior coronary heart disease (CHD,) were excluded for CHD analysis. Models shown in A or B are from the same population and differ by having covariates either removed or added. All individuals were from Jackson Heart Study. n = 1,795 Model 1 Model 2 Model 3 Covariate HR (95% CI) p-value HR (95% CI) p-value HR (95% CI) p-value log (Age) 3.3 (0.6-17) 0.17 2.7 (0.5-13) 0.23 2.8 (0.6-14) 0.21 Has T2D 2.9 (1.4-5.9) 0.004   3 (1.5-6) 0.002   3 (16-5.8) <0.001 Female 0.7 (0.3-1.3) 0.21 0.7 (0.3-1.3) 0.21 0.6 (0.3-1.2) 0.13 HDL <35 mg/dL 1.6 (0.6-4.6) 0.38 1.8 (0.6-5.1) 0.29 HDL ≥60 mg/dL   1 (0.5-2.1) 0.99   1 (0.5-2) 0.9 TC >200 mg/dL 2.2 (1.1-4.1) 0.021 2.3 (1.2-4.6) 0.015 2.3 (1.2-4.5) 0.013 Former or current   2 (1.1-3.7) 0.027 2.1 (1.1-3.9) 0.021   2 (1.1-3.8) 0.024 smoker SBP ≥160 mm Hg 2.4 (1.2-4.7) 0.011 2.2 (1.1-4.4) 0.023 2.2 (1.1-4.3) 0.03 hsCRP >1.0 mg/L 1.1 (0.5-2.5) 0.76   1 (0.4-2.3) 0.99 No clone, 2.3 (1.2-4.6) 0.017 2.2 (1.1-4.4) 0.02 RDW ≥14.5% Clone, 0.9 (0.1-7.4) 0.95 1.7 (0.4-7.7) 0.46 RDW <14.5% Clone, 9.8 (2-48.3) 0.005 9.3 (2-43) 0.005 RDW ≥14.5% Pseudo Log-likelihood −273 −268 −276 Pseudo likelihood ratio test 39.1 on 9 df 49.0 on 12 df 48.0 on 9 df

Having thus described in detail preferred embodiments of the present invention, it is to be understood that the invention defined by the above paragraphs is not to be limited to particular details set forth in the above description an many apparent variations thereof are possible without departing from the spirit or scope of the present invention. 

What is claimed is:
 1. A method of treating or preventing coronary heart disease (CHD) in a human subject, comprising the steps of: (a) sequencing at least part of a genome of one or more cells in a blood sample of the subject, wherein the at least part of said genome comprises a DNMT3A gene, (b) detecting a loss of function mutation in the DNMT3A gene selected from a frameshift mutation and a nonsense mutation; and (c) administering a lipid modifying medicine to the subject.
 2. The method according to claim 1, wherein the presence of said mutation indicates an increase in red blood cell distribution width (RDW).
 3. The method according to claim 1, wherein the subject has CHD.
 4. The method according to claim 1, wherein the one more cells in the blood sample are hematopoietic stem cells (HSCs), committed myeloid progenitor cells having long term self-renewal capacity or mature lymphoid cells having long term self-renewal capacity.
 5. The method according to claim 1, wherein the part of the genome is an exome.
 6. The method according to claim 1, wherein the sequencing is whole exome sequencing (WES).
 7. The method according to claim 1, wherein the subject also exhibits one or more risk factors of being a smoker, having a high level of total cholesterol or having high level of high-density lipoprotein (HDL).
 8. The method of claim 1, wherein the lipid-modifying medicine is a statin.
 9. The method of claim 1, wherein the lipid-modifying medicine is a PCSK9 inhibitor.
 10. The method of claim 9, wherein the PCSK9 inhibitor is a monoclonal antibody.
 11. A method for providing personalized medicine to a human subject with increased risk of CHD, said method comprising the steps of (a) sequencing at least part of a genome of one or more cells in a blood sample of the subject, wherein the at least part of the genome comprises a DMNT3A gene, (b) detecting a loss of function-mutation in the DNMT3A gene selected from a frameshift mutation and a nonsense mutation; and (c) administering a lipid-modifying medicine to the subject.
 12. The method according to claim 11, wherein the subject has CHD.
 13. The method according to claim 11, wherein the one more cells in the blood sample are hematopoietic stem cells (HSCs), committed myeloid progenitor cells having long term self-renewal capacity or mature lymphoid cells having long term self-renewal capacity.
 14. The method according to claim 11, wherein the part of the genome is an exome.
 15. The method according to claim 11, wherein the sequencing is whole exome sequencing (WES).
 16. The method according to claim 11, wherein the subject also exhibits one or more risk factors of being a smoker, having a high level of total cholesterol or having high level of high-density lipoprotein (HDL).
 17. The method of claim 11, wherein the lipid-modifying medicine is a statin.
 18. The method of claim 11, wherein the lipid-modifying medicine is a PCSK9 inhibitor.
 19. The method of claim 18, wherein the PCSK9 inhibitor is a monoclonal antibody.
 20. A method of treating or preventing coronary heart disease (CHD) in a human subject, wherein the genome of a blood cell in said subject comprises a loss of function mutation in a DNMT3A gene selected from a frameshift mutation and a nonsense mutation, the method comprising administering a lipid-modifying medicine to the subject.
 21. The method of claim 20, wherein the lipid-modifying medicine is a statin.
 22. The method of claim 20, wherein the lipid-modifying medicine is a PCSK9 inhibitor.
 23. The method of claim 22, wherein the PCSK9 inhibitor is a monoclonal antibody. 